machine learning interview case study

Data Science Interview Practice: Machine Learning Case Study

A common interview type for data scientists and machine learning engineers is the machine learning case study. In it, the interviewer will ask a question about how the candidate would build a certain model. These questions can be challenging for new data scientists because the interview is open-ended and new data scientists often lack practical experience building and shipping product-quality models.

I have a lot of practice with these types of interviews as a result of my time at Insight , my many experiences interviewing for jobs , and my role in designing and implementing Intuit’s data science interview. Similar to my last article where I put together an example data manipulation interview practice problem , this time I will walk through a practice case study and how I would work through it.

My Approach

Case study interviews are just conversations. This can make them tougher than they need to be for junior data scientists because they lack the obvious structure of a coding interview or data manipulation interview . I find it’s helpful to impose my own structure on the conversation by approaching it in this order:

• Problem : Dive in with the interviewer and explore what the problem is. Look for edge cases or simple and high-impact parts of the problem that you might be able to close out quickly.
• Metrics : Once you have determined the scope and parameters of the problem you’re trying to solve, figure out how you will measure success. Focus on what is important to the business and not just what is easy to measure.
• Data : Figure out what data is available to solve the problem. The interviewer might give you a couple of examples, but ask about additional information sources. If you know of some public data that might be useful, bring it up here too.
• Labels and Features : Using the data sources you discussed, what features would you build? If you are attacking a supervised classification problem, how would you generate labels? How would you see if they were useful?
• Model : Now that you have a metric, data, features, and labels, what model is a good fit? Why? How would you train it? What do you need to watch out for?
• Validation : How would you make sure your model works offline? What data would you hold out to test your model works as expected? What metrics would you measure?
• Deployment and Monitoring : Having developed a model you are comfortable with, how would you deploy it? Does it need to be real-time or is it sufficient to batch inputs and periodically run the model? How would you check performance in production? How would you monitor for model drift where its performance changes over time?

Here is the prompt:

At Twitter, bad actors occasionally use automated accounts, known as “bots”, to abuse our platform. How would you build a system to help detect bot accounts?

At the start of the interview I try to fully explore the bounds of the problem, which is often open ended. My goal with this part of the interview is to:

• Understand the problem and all the edges cases.
• Come to an agreement with the interviewer on the scope—narrower is better!—of the problem to solve.
• Demonstrate any knowledge I have on the subject, especially from researching the company previously.

Our Twitter bot prompt has a lot of angles from which we could attack. I know Twitter has dozens of types of bots, ranging from my harmless Raspberry Pi bots , to “Russian Bots” trying to influence elections , to bots spreading spam . I would pick one problem to focus on using my best guess as to business impact. In this case spam bots are likely a problem that causes measurable harm (drives users away, drives advertisers away). Russian bots are probably a bigger issue in terms of public perception, but that’s much harder to measure.

After deciding on the scope, I would ask more about the systems they currently have to deal with it. Likely Twitter has an ops team to help identify spam and block accounts and they may even have a rules based system. Those systems will be a good source of data about the bad actors and they likely also have metrics they track for this problem.

Having agreed on what part of the problem to focus on, we now turn to how we are going to measure our impact. There is no point shipping a model if you can’t measure how it’s affecting the business.

Metrics and model use go hand-in-hand, so first we have to agree on what the model will be used for. For spam we could use the model to just mark suspected accounts for human review and tracking, or we could outright block accounts based on the model result. If we pick the human review option, it’s probably more important to get all the bots even if some good customers are affected. If we go with immediate action, it is likely more important to only ban truly bad accounts. I covered thinking about metrics like this in detail in another post, What Machine Learning Metric to Use . Take a look!

I would argue the automatic blocking model will have higher impact because it frees our ops people to focus on other bad behavior. We want two sets of metrics: offline for when we are training and online for when the model is deployed.

Our offline metric will be precision because, based on the argument above, we want to be really sure we’re only banning bad accounts.

Our online metrics are more business focused:

• Ops time saved : Ops is currently spending some amount of time reviewing spam; how much can we cut that down?
• Spam fraction : What percent of Tweets are spam? Can we reduce this?

It is often useful to normalize metrics, like the spam fraction metric, so they don’t go up or down just because we have more customers!

Now that we know what we’re doing and how to measure its success, it’s time to figure out what data we can use. Just based on how a company operates, you can make a really good guess as to the data they have. For Twitter we know they have to track Tweets, accounts, and logins, so they must have databases with that information. Here are what I think they contain:

• Tweets database : Sending account, mentioned accounts, parent Tweet, Tweet text.
• Interactions database : Account, Tweet, action (retweet, favorite, etc.).
• Accounts database : Account name, handle, creation date, creation device, creation IP address.
• Following database : Account, followed account.
• Login database : Account, date, login device, login IP address, success or fail reason.
• Ops database : Account, restriction, human reasoning.

And a lot more. From these we can find out a lot about an account and the Tweets they send, who they send to, who those people react to, and possibly how login events tie different accounts together.

Labels and Features

Having figured out what data is available, it’s time to process it. Because I’m treating this as a classification problem, I’ll need labels to tell me the ground truth for accounts, and I’ll need features which describe the behavior of the accounts.

Since there is an ops team handling spam, I have historical examples of bad behavior which I can use as positive labels. 1 If there aren’t enough I can use tricks to try to expand my labels, for example looking at IP address or devices that are associated with spammers and labeling other accounts with the same login characteristics.

Negative labels are harder to come by. I know Twitter has verified users who are unlikely to be spam bots, so I can use them. But verified users are certainly very different from “normal” good users because they have far more followers.

It is a safe bet that there are far more good users than spam bots, so randomly selecting accounts can be used to build a negative label set.

To build features, it helps to think about what sort of behavior a spam bot might exhibit, and then try to codify that behavior into features. For example:

• Bots can’t write truly unique messages ; they must use a template or language generator. This should lead to similar messages, so looking at how repetitive an account’s Tweets are is a good feature.
• Bots are used because they scale. They can run all the time and send messages to hundreds or thousands (or millions) or users. Number of unique Tweet recipients and number of minutes per day with a Tweet sent are likely good features.
• Bots have a controller. Someone is benefiting from the spam, and they have to control their bots. Features around logins might help here like number of accounts seen from this IP address or device, similarity of login time, etc.

Model Selection

I try to start with the simplest model that will work when starting a new project. Since this is a supervised classification problem and I have written some simple features, logistic regression or a forest are good candidates. I would likely go with a forest because they tend to “just work” and are a little less sensitive to feature processing. 2

Deep learning is not something I would use here. It’s great for image, video, audio, or NLP, but for a problem where you have a set of labels and a set of features that you believe to be predictive it is generally overkill.

One thing to consider when training is that the dataset is probably going to be wildly imbalanced. I would start by down-sampling (since we likely have millions of events), but would be ready to discuss other methods and trade offs.

Validation is not too difficult at this point. We focus on the offline metric we decided on above: precision. We don’t have to worry much about leaking data between our holdout sets if we split at the account level, although if we include bots from the same botnet into our different sets there will be a little data leakage. I would start with a simple validation/training/test split with fixed fractions of the dataset.

Since we want to classify an entire account and not a specific tweet, we don’t need to run the model in real-time when Tweets are posted. Instead we can run batches and can decide on the time between runs by looking at something like the characteristic time a spam bot takes to send out Tweets. We can add rate limiting to Tweet sending as well to slow the spam bots and give us more time to decide without impacting normal users.

For deployment, I would start in shadow mode , which I discussed in detail in another post . This would allow us to see how the model performs on real data without the risk of blocking good accounts. I would track its performance using our online metrics: spam fraction and ops time saved. I would compute these metrics twice, once using the assumption that the model blocks flagged accounts, and once assuming that it does not block flagged accounts, and then compare the two outcomes. If the comparison is favorable, the model should be promoted to action mode.

Let Me Know!

I hope this exercise has been helpful! Please reach out and let me know at @alex_gude if you have any comments or improvements!

In this case a positive label means the account is a spam bot, and a negative label means they are not.  ↩

If you use regularization with logistic regression (and you should) you need to scale your features. Random forests do not require this.  ↩

Practice Interview Questions

70 Machine Learning Interview Questions & Answers

By Nick Singh

(Ex-Facebook & Best-Selling Data Science Author)

Currently, he’s the best-selling author of Ace the Data Science Interview, and Founder & CEO of DataLemur.

February 12, 2024

Are you gearing up for a machine learning interview and feeling a bit overwhelmed?

Fear not! In this comprehensive guide, we've compiled 70 machine-learning interview questions and their detailed answers to help you ace your next interview with confidence. Let's dive in and unravel the secrets to mastering machine learning interviews.

Fundamental Concepts

Questions in this section may cover basic concepts such as supervised learning, unsupervised learning, reinforcement learning, model evaluation metrics, bias-variance tradeoff, overfitting, underfitting, cross-validation, and regularization techniques like L1 and L2 regularization.

Question 1: What is the bias-variance tradeoff in machine learning?

Answer: The bias-variance tradeoff refers to the balance between bias and variance in predictive models. High bias can cause underfitting, while high variance can lead to overfitting. It's crucial to find a balance to minimize both errors.

Question 2: Explain cross-validation and its importance in model evaluation.

Answer: Cross-validation is a technique used to assess how well a predictive model generalizes to unseen data by splitting the dataset into multiple subsets for training and testing. It helps in detecting overfitting and provides a more accurate estimate of a model's performance.

Question 3: What is regularization, and how does it prevent overfitting?

Answer: Regularization is a technique used to prevent overfitting by adding a penalty term to the loss function, discouraging the model from fitting the training data too closely. Common regularization techniques include L1 (Lasso) and L2 (Ridge) regularization.

Question 4: What are evaluation metrics commonly used for classification tasks?

Answer: Evaluation metrics for classification tasks include accuracy, precision, recall, F1-score, ROC curve, and AUC-ROC score. Each metric provides insights into different aspects of the model's performance.

Question 5: What is the difference between supervised and unsupervised learning?

Answer: Supervised learning involves training a model on labeled data, where the algorithm learns the mapping between input and output variables. In contrast, unsupervised learning deals with unlabeled data and aims to find hidden patterns or structures in the data.

Question 6: How do you handle imbalanced datasets in machine learning?

Answer: Techniques for handling imbalanced datasets include resampling methods such as oversampling minority class instances or under sampling majority class instances, using different evaluation metrics like precision-recall curves, and employing algorithms specifically designed for imbalanced data, such as SMOTE (Synthetic Minority Over-sampling Technique).

Question 7: What is the purpose of feature scaling in machine learning?

Answer: Feature scaling ensures that all features contribute equally to the model training process by scaling them to a similar range. Common scaling techniques include min-max scaling and standardization (Z-score normalization).

Question 8: Explain the concept of overfitting and underfitting in machine learning.

Answer: Overfitting occurs when a model learns the training data too well, capturing noise and irrelevant patterns, leading to poor generalization on unseen data. Underfitting, on the other hand, happens when a model is too simple to capture the underlying structure of the data, resulting in low performance on both training and testing data.

Question 9: What is the difference between a parametric and non-parametric model?

Answer: Parametric models make assumptions about the functional form of the relationship between input and output variables and have a fixed number of parameters. Non-parametric models do not make such assumptions and can adapt to the complexity of the data, often having an indefinite number of parameters.

Question 10: How do you assess the importance of features in a machine-learning model?

Answer: Feature importance can be assessed using techniques like examining coefficients in linear models, feature importance scores in tree-based models, or permutation importance. These methods help identify which features have the most significant impact on the model's predictions.

Data Preprocessing and Feature Engineering

This section may include questions about data cleaning techniques, handling missing values, scaling features, encoding categorical variables, feature selection methods, dimensionality reduction techniques like PCA (Principal Component Analysis), and dealing with imbalanced datasets.

Question 1: What are some common techniques for handling missing data?

Answer: Common techniques for handling missing data include imputation (replacing missing values with estimated values such as mean, median, or mode), deletion of rows or columns with missing values, or using advanced methods like predictive modeling to fill missing values.

Question 2: How do you deal with categorical variables in a machine-learning model?

Answer: Categorical variables can be encoded using techniques like one-hot encoding, label encoding, or target encoding, depending on the nature of the data and the algorithm being used. One-hot encoding creates binary columns for each category, while label encoding assigns a unique integer to each category.

Question 3: What is feature scaling, and when is it necessary?

Answer: Feature scaling is the process of standardizing or normalizing the range of features in the dataset. It is necessary when features have different scales, as algorithms like gradient descent converge faster and more reliably when features are scaled to a similar range.

Question 4: How do you handle outliers in a dataset?

Answer: Outliers can be handled by removing them if they are due to errors or extreme values, transforming the data using techniques like logarithmic or square root transformations, or using robust statistical methods that are less sensitive to outliers.

Question 5: What is feature selection, and why is it important?

Answer: Feature selection is the process of choosing the most relevant features for building predictive models while discarding irrelevant or redundant ones. It is important because it reduces the dimensionality of the dataset, improves model interpretability, and prevents overfitting.

Question 6: Explain the concept of dimensionality reduction.

Answer: Dimensionality reduction techniques like PCA (Principal Component Analysis) and t-SNE (t-distributed Stochastic Neighbor Embedding) are used to reduce the number of features in a dataset while preserving its essential characteristics. This helps in visualization, data compression, and speeding up the training process of machine learning models.

Question 7: What are some methods for detecting and handling multicollinearity among features?

Answer: Multicollinearity occurs when two or more features in a dataset are highly correlated, which can cause issues in model interpretation and stability. Methods for detecting and handling multicollinearity include correlation matrices, variance inflation factor (VIF) analysis, and feature selection techniques.

Question 8: How do you handle skewed distributions in features?

Answer: Skewed distributions can be transformed using techniques like logarithmic transformation, square root transformation, or Box-Cox transformation to make the distribution more symmetrical and improve model performance, especially for algorithms that assume normality.

Question 9: What is the curse of dimensionality, and how does it affect machine learning algorithms?

Answer: The curse of dimensionality refers to the increased computational and statistical challenges associated with high-dimensional data. As the number of features increases, the amount of data required to generalize accurately grows exponentially, leading to overfitting and decreased model performance.

Question 10: When should you use feature engineering techniques like polynomial features?

Answer: Polynomial features are useful when the relationship between the independent and dependent variables is non-linear. By creating polynomial combinations of features, models can capture more complex relationships, improving their ability to fit the data.

Supervised Learning Algorithms

Questions here may focus on various supervised learning algorithms such as linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-nearest neighbors (KNN), naive Bayes, gradient boosting methods like XGBoost, and neural networks.

Question 1: Explain the difference between regression and classification algorithms.

Answer: Regression algorithms are used to predict continuous numeric values, while classification algorithms are used to predict categorical labels or classes. Examples of regression algorithms include linear regression and polynomial regression, while examples of classification algorithms include logistic regression, decision trees, and support vector machines.

Question 2: How does a decision tree work, and what are its advantages and disadvantages?

Answer: A decision tree is a tree-like structure where each internal node represents a decision based on a feature, and each leaf node represents the outcome or prediction. Its advantages include interpretability, ease of visualization, and handling both numerical and categorical data. However, it is prone to overfitting, especially with complex trees.

Question 3: What is the difference between bagging and boosting?

Answer: Bagging (Bootstrap Aggregating) and boosting are ensemble learning techniques used to improve model performance by combining multiple base learners. Bagging trains each base learner independently on different subsets of the training data, while boosting focuses on training base learners sequentially, giving more weight to misclassified instances.

Question 4: Explain the working principle of support vector machines (SVM).

Answer: Support Vector Machines (SVM) is a supervised learning algorithm used for classification and regression tasks. It works by finding the hyperplane that best separates the data points into different classes while maximizing the margin, which is the distance between the hyperplane and the nearest data points from each class.

Question 5: What is logistic regression, and when is it used?

Answer: Logistic regression is a binary classification algorithm used to predict the probability of a binary outcome based on one or more predictor variables. It is commonly used when the dependent variable is categorical (e.g., yes/no, true/false) and the relationship between the independent and dependent variables is linear.

Question 6: Explain the concept of ensemble learning and its advantages.

Answer: Ensemble learning combines predictions from multiple models to improve overall performance. It can reduce overfitting, increase predictive accuracy, and handle complex relationships in the data better than individual models. Examples include random forests, gradient boosting machines (GBM), and stacking.

Question 7: How does linear regression handle multicollinearity among features?

Answer: Multicollinearity among features in linear regression can lead to unstable coefficient estimates and inflated standard errors. Techniques for handling multicollinearity include removing correlated features, using regularization techniques like ridge regression, or employing dimensionality reduction methods like PCA.

Question 8: What is the difference between gradient descent and stochastic gradient descent?

Answer: Gradient descent is an optimization algorithm used to minimize the loss function by iteratively adjusting model parameters in the direction of the steepest descent of the gradient. Stochastic gradient descent (SGD) is a variant of gradient descent that updates the parameters using a single randomly chosen data point or a small batch of data points at each iteration, making it faster and more suitable for large datasets.

Question 9: When would you use a decision tree versus a random forest?

Answer: Decision trees are simple and easy to interpret but are prone to overfitting. Random forests, which are ensembles of decision trees, reduce overfitting by averaging predictions from multiple trees and provide higher accuracy and robustness, especially for complex datasets with many features.

Question 10: What is the purpose of hyperparameter tuning in machine learning algorithms?

Answer: Hyperparameter tuning involves selecting the optimal values for hyperparameters, which are parameters that control the learning process of machine learning algorithms. It helps improve model performance by finding the best configuration of hyperparameters through techniques like grid search, random search, or Bayesian optimization.

Unsupervised Learning Algorithms

This section might involve questions about unsupervised learning algorithms like k-means clustering, hierarchical clustering, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), Gaussian Mixture Models (GMM), and dimensionality reduction techniques like t-SNE (t-distributed Stochastic Neighbor Embedding).

Question 1: Explain the k-means clustering algorithm and its steps.

Answer: K-means clustering is a partitioning algorithm that divides a dataset into k clusters by minimizing the sum of squared distances between data points and their respective cluster centroids. The steps include initializing cluster centroids, assigning data points to the nearest centroid, updating centroids, and iterating until convergence.

Question 2: What is the difference between k-means and hierarchical clustering?

Answer: K-means clustering partitions the dataset into a predefined number of clusters (k) by minimizing the within-cluster variance, while hierarchical clustering builds a hierarchy of clusters by recursively merging or splitting clusters based on similarity or dissimilarity measures.

Question 3: How does DBSCAN clustering work, and what are its advantages?

Answer: DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm that groups data points that are closely packed while marking outliers as noise. Its advantages include the ability to discover clusters of arbitrary shapes, robustness to noise and outliers, and not requiring the number of clusters as input.

Question 4: Explain the working principle of Gaussian Mixture Models (GMM).

Answer: Gaussian Mixture Models (GMM) represent the probability distribution of a dataset as a mixture of multiple Gaussian distributions, each associated with a cluster. The model parameters, including means and covariances of the Gaussians, are estimated using the Expectation-Maximization (EM) algorithm.

Question 5: When would you use hierarchical clustering over k-means clustering?

Answer: Hierarchical clustering is preferred when the number of clusters is unknown or when the data exhibits a hierarchical structure, as it produces a dendrogram that shows the relationships between clusters at different levels of granularity. In contrast, k-means clustering requires specifying the number of clusters in advance and may not handle non-spherical clusters well.

Question 6: What are the advantages and disadvantages of unsupervised learning?

Answer: The advantages of unsupervised learning include its ability to discover hidden patterns or structures in data without labeled examples, making it useful for exploratory data analysis and feature extraction. However, its disadvantages include the lack of ground truth labels for evaluation and the potential for subjective interpretation of results.

Question 7: How do you determine the optimal number of clusters in a clustering algorithm?

Answer: The optimal number of clusters can be determined using techniques like the elbow method, silhouette analysis, or the gap statistic. These methods aim to find the point where adding more clusters does not significantly improve the clustering quality or where the silhouette score is maximized.

Question 8: What is the purpose of dimensionality reduction in unsupervised learning?

Answer: Dimensionality reduction techniques like PCA (Principal Component Analysis) and t-SNE (t-distributed Stochastic Neighbor Embedding) are used in unsupervised learning to reduce the number of features in a dataset while preserving its essential characteristics. This helps in visualization, data compression, and speeding up the training process of machine learning models.

Question 9: How do you handle missing values in unsupervised learning?

Answer: In unsupervised learning, missing values can be handled by imputation techniques like mean, median, or mode imputation, or by using advanced methods like k-nearest neighbors (KNN) imputation or matrix factorization.

Question 10: What are some applications of unsupervised learning in real-world scenarios?

Answer: Some applications of unsupervised learning include customer segmentation for targeted marketing, anomaly detection in cybersecurity, topic modeling for text analysis, image clustering for visual content organization, and recommendation systems for personalized content delivery.

Deep Learning

Questions in this section may cover topics related to deep learning architectures such as convolutional neural networks (CNNs) for image data, recurrent neural networks (RNNs) for sequential data, long short-term memory networks (LSTMs), attention mechanisms, transfer learning, and popular deep learning frameworks like TensorFlow and PyTorch.

Question 1: What are the key components of a neural network?

Answer: The key components of a neural network include an input layer, one or more hidden layers, each consisting of neurons or nodes, and an output layer. Each neuron applies an activation function to the weighted sum of its inputs to produce an output.

Question 2: Explain the working principle of convolutional neural networks (CNNs).

Answer: Convolutional Neural Networks (CNNs) are specialized neural networks designed for processing structured grid-like data, such as images. They consist of convolutional layers that extract features from input images, pooling layers that downsample feature maps, and fully connected layers that classify the extracted features.

Question 3: What is the purpose of activation functions in neural networks?

Answer: Activation functions introduce non-linearity into the neural network, enabling it to learn complex patterns and relationships in the data. Common activation functions include ReLU (Rectified Linear Unit), sigmoid, tanh (hyperbolic tangent), and softmax.

Question 4: How do you prevent overfitting in deep learning models?

Answer: Techniques for preventing overfitting in deep learning models include using dropout layers to randomly deactivate neurons during training, adding L1 or L2 regularization to penalize large weights, collecting more training data, and early stopping based on validation performance.

Question 5: Explain the concept of transfer learning in deep learning.

Answer: Transfer learning is a technique where a pre-trained neural network model is reused for a different but related task. By leveraging knowledge learned from a large dataset or task, transfer learning allows the model to achieve better performance with less training data and computational resources.

Question 6: What is the difference between shallow and deep neural networks?

Answer: Shallow neural networks have only one hidden layer between the input and output layers, while deep neural networks have multiple hidden layers. Deep neural networks can learn hierarchical representations of data, capturing complex patterns and relationships, but they require more computational resources and may suffer from vanishing or exploding gradients.

Question 7: How do recurrent neural networks (RNNs) handle sequential data?

Answer: Recurrent Neural Networks (RNNs) process sequential data by maintaining a hidden state that captures information from previous time steps and updates it recursively as new input is fed into the network. This allows RNNs to model temporal dependencies and sequences of variable length.

Question 8: What is the vanishing gradient problem, and how does it affect deep learning?

Answer: The vanishing gradient problem occurs when gradients become increasingly small as they propagate backward through layers in deep neural networks during training, making it difficult to update the weights of early layers effectively. It can lead to slow convergence or stagnation in learning.

Question 9: What are some popular deep learning frameworks, and why are they used?

Answer: Popular deep learning frameworks include TensorFlow, PyTorch, Keras, and MXNet. These frameworks provide high-level APIs and abstractions for building and training neural networks, allowing researchers and practitioners to focus on model design and experimentation rather than low-level implementation details.

Question 10: How do you choose the appropriate neural network architecture for a given problem?

Answer: Choosing the appropriate neural network architecture depends on factors such as the nature of the data (e.g., structured, unstructured), the complexity of the problem, computational resources available, and the trade-off between model performance and interpretability. Experimentation and validation on a held-out dataset are essential for selecting the best architecture.

Model Evaluation and Performance Tuning

This section may include questions about techniques for evaluating model performance such as accuracy, precision, recall, F1-score, ROC curve, AUC-ROC score, and strategies for hyperparameter tuning using techniques like grid search, random search, and Bayesian optimization.

Question 1: What evaluation metrics would you use for a binary classification problem, and why?

Answer: For a binary classification problem, common evaluation metrics include accuracy, precision, recall, F1-score, ROC curve, and AUC-ROC score. These metrics provide insights into different aspects of the model's performance, such as overall correctness, class-wise performance, and trade-offs between true positive and false positive rates.

Question 2: How do you interpret the ROC curve and AUC-ROC score?

Answer: The ROC curve plots the true positive rate (sensitivity) against the false positive rate (1-specificity) at various threshold settings, showing the trade-offs between sensitivity and specificity. The AUC-ROC score represents the area under the ROC curve, with higher values indicating better discrimination performance of the model.

Question 3: What is the purpose of cross-validation, and how does it work?

Answer: Cross-validation is a technique used to assess how well a predictive model generalizes to unseen data by splitting the dataset into multiple subsets for training and testing. It works by iteratively training the model on a subset of the data (training set) and evaluating its performance on the remaining data (validation set), rotating the subsets until each subset has been used as both training and validation data.

Question 4: What is hyperparameter tuning, and why is it important?

Answer: Hyperparameter tuning involves selecting the optimal values for hyperparameters, which are parameters that control the learning process of machine learning algorithms. It is important because the choice of hyperparameters can significantly affect the performance of the model, and finding the best configuration can improve predictive accuracy and generalization.

Question 5: How would you approach model selection for a given problem?

Answer: Model selection involves comparing the performance of different models on a validation dataset and selecting the one with the best performance based on evaluation metrics relevant to the problem at hand. It requires experimentation with different algorithms, architectures, and hyperparameter settings to identify the model that generalizes well to unseen data.

Question 6: What is overfitting, and how can it be detected and prevented?

Answer: Overfitting occurs when a model learns the training data too well, capturing noise and irrelevant patterns, leading to poor generalization on unseen data. It can be detected by comparing the performance of the model on training and validation datasets or using techniques like cross-validation. To prevent overfitting, regularization techniques like L1 or L2 regularization, dropout, and early stopping can be applied.

Question 7: How do you perform feature selection to improve model performance?

Answer: Feature selection involves choosing the most relevant features for building predictive models while discarding irrelevant or redundant ones. It can be performed using techniques like univariate feature selection, recursive feature elimination, or model-based feature selection, based on criteria such as feature importance scores or statistical tests.

Question 8: What is grid search, and how does it work?

Answer: Grid search is a hyperparameter tuning technique that exhaustively searches through a specified grid of hyperparameter values, evaluating the model's performance using cross-validation for each combination of hyperparameters. It helps identify the optimal hyperparameter values that maximize the model's performance.

Question 9: How would you handle class imbalance in a classification problem?

Answer: Techniques for handling class imbalance in classification problems include resampling methods such as oversampling the minority class or undersampling the majority class, using different evaluation metrics like precision-recall curves or AUC-ROC score, and employing algorithms specifically designed for imbalanced data, such as SMOTE (Synthetic Minority Over-sampling Technique).

Question 10: What is early stopping, and how does it prevent overfitting?

Answer: Early stopping is a technique used to prevent overfitting by monitoring the model's performance on a validation dataset during training and stopping the training process when the performance starts deteriorating. It works by halting training before the model becomes overly specialized to the training data, thus improving generalization.

Real-World Applications and Case Studies

Candidates may be asked to discuss real-world machine learning applications they have worked on, challenges faced during projects, how they approached problem-solving, and their understanding of the broader implications and ethical considerations of deploying machine learning systems.

Question 1: Can you describe a machine learning project you worked on and the challenges you faced?

Answer: Candidate's response about a specific project, including the problem statement, data used, algorithms employed, challenges encountered, and how they addressed them.

Question 2: What are some ethical considerations to keep in mind when deploying machine learning systems in real-world applications?

Answer: Candidate's response discussing ethical considerations such as bias and fairness, privacy and data protection, transparency and accountability, and potential societal impacts of machine learning systems.

Question 3: How would you approach building a recommendation system for an e-commerce platform?

Answer: Candidate's response outlining the steps involved in building a recommendation system, including data collection, preprocessing, algorithm selection, evaluation metrics, and deployment considerations.

Question 4: Can you discuss a time when you had to work with a large dataset and how you handled it?

Answer: Candidate's response describing their experience working with large datasets, including data preprocessing, optimization techniques, distributed computing frameworks, and strategies for efficient data storage and retrieval.

Question 5: What are some challenges you foresee in deploying a machine-learning model into production?

Answer: Candidate's response discussing challenges such as model scalability, performance monitoring, version control, model drift, security considerations, and integration with existing systems.

Question 6: Can you explain a situation where feature engineering played a crucial role in improving model performance?

Answer: Candidate's response providing an example of feature engineering techniques applied to a specific problem, including feature selection, transformation, creation of new features, and their impact on model performance.

Question 7: How would you evaluate the impact of a machine learning model on a business outcome?

Answer: Candidate's response discussing metrics for evaluating the business impact of a machine learning model, such as return on investment (ROI), cost savings, revenue generation, customer satisfaction, and user engagement.

Question 8: What are some considerations for deploying a machine learning model in a resource-constrained environment?

Answer: Candidate's response addressing considerations such as model size and complexity, computational resource requirements, latency and throughput constraints, energy efficiency, and trade-offs between model performance and deployment feasibility.

Question 9: Can you describe a scenario where you had to explain complex machine-learning concepts to a non-technical audience?

Answer: Candidate's response describing their experience communicating complex machine learning concepts clearly and understandably to stakeholders, clients, or team members with varying levels of technical expertise.

Question 10: How do you stay updated with the latest advancements and trends in machine learning?

Answer: Candidate's response discussing their strategies for staying updated with the latest advancements and trends in machine learning, such as attending conferences, reading research papers, participating in online courses, and experimenting with new techniques and frameworks.

Additional Resources

Need more resources? I HIGHLY recommend my Ace the Data Job Hunt video course. This course is filled with 25+ videos as well as downloadable resources, that will help you get the job you want.

BTW, companies also go HARD on technical interviews – it's not just Machine Learning interviews that are a must to prepare. Test yourself and solve over 200+ SQL questions on Data Lemur which come from companies like Facebook, Google, and VC-backed startups.

But if your SQL coding skills are weak, forget about going right into solving questions – refresh your SQL knowledge with this DataLemur SQL Tutorial .

I'm a bit biased, but I also recommend the book Ace the Data Science Interview because it has multiple FAANG technical Interview questions with solutions in it.

Interview Questions

Career resources.

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[2023] Machine Learning Interview Prep

Got a machine learning interview lined up? Chances are that you are interviewing for ML engineering and/or data scientist position. Companies that have ML interview portions are  Google ,  Meta ,  Stripe ,  McKinsey , and startups. And, the ML questions are peppered throughout the technical screen, take-home, and on-site rounds. So, what are entailed in the ML engineering interview? There are generally five areas👇

📚 ML Interview A reas

Area 1 – ML Coding

ML coding is similar to LeetCode style, but the main difference is that it is the application of machine learning using coding. Expect to write ML functions from scratch. In some cases, you will not be allowed to import third-party libraries like SkLearn as the questions are designed to assess your conceptual understanding and coding ability.

Area 2 – ML Theory (”Breath”)

These assess the candidate’s breath of knowledge in machine learning. Conceptual understanding of ML theories including the bias-variance trade-off, handling imbalanced labels, and accuracy vs interpretability are what’s assessed in ML theory interviews.

Area 3 – ML Algorithms (”Depth”)

Don’t confuse ML algorithms (sometimes called “Depth”) as the same coverage as ML “Breath”. While ML breath covers the general understanding of machine learning. ML Depth, on the other hand, assesses an in-depth understanding of the particular algorithm. For instance, you may have a dedicated round just focusing on the random forest. E.g. Here’s a sample question set you could be asked in a single round at Amazon.

Area 4 – Applied ML / Business Case

These are solve ML cases in the context of a business problem. Scalability and productionization are not the main concern as they are more so relevant in ML system design portions. Business case could be assessed in various form; it could be verbal explanation, or hands-on coding on Jupyter or Colab.

Area 5 – ML System Design

These assess the soundness and scalability of the ML system design. They are often assessed in the ML engineering interview, and you will be required to discuss the functional & non-functional requirements, architecture overview, data preparation, model training, model evaluation, and model productionization.

📚 ML Questions x Track (e.g. product analyst, data scientist, MLE)

Depending on the tracks, the type of ML questions you will be exposed to will vary. Here are some examples. Consider the following questions posed in various roles:

• Product Analyst  – Build a model that can predict the lifetime value of a customer
• Data Scientist (Generalist)  – Build a fraud detection model using credit card transactions
• ML Engineering  – Build a recommender system that can scale to 10 million daily active users

For product analyst roles, the emphasis is on the application of ML on product analysis, user segmentation, and feature improvement. Rigor in scalable system is not required as most of the analysis is conducted on offline dataset.

For data scientist roles, you will most likely be assessed on ML breath, depth, and business case challenges. Understanding scalable systems is not required unless the role is more focused on “full-stack” type of data science role.

For ML engineering role, you will be asked coding, ML breath & depth and ML system design design questions. You will most likely have dedicated rounds on ML coding and ML system design with ML breath & depth questions peppered throughout the interview process.

✍️  7 Algorithms You Should Know

In general you should have a in-depth understanding of the following algorithms. Understand the assumption, application, trade-offs and parameter tuning of these 7 ML algorithms. The most important aspect isn’t whether you understand 20+ ML algorithms. What’s more important is that you understand how to leverage 7 algorithms in 20 different situations.

• Linear Regression
• Logistic Regression
• Decision Tree
• Random Forest
• Gradient Boosted Trees
• Dense Neural Networks

📝 More Questions

• What is the difference between supervised and unsupervised learning?
• Can you explain the concept of overfitting and underfitting in machine learning models?
• What is cross-validation? Why is it important?
• Describe how a decision tree works. When would you use it over other algorithms?
• What is the difference between bagging and boosting?
• How would you validate a model you created to generate a predictive analysis?
• How does KNN work?
• What is PCA?
• How would you perform feature selection?
• What are the advantages and disadvantages of a neural network?

💡 Prep Tips

Tip 1 – Understand How ML Interviews are Screen

The typical format is 20 to 40 minutes embedded in a technical phone screen or a dedicated ML round within an onsite. You will be assessed by Sr./Staff-level data scientist or ML engineer. Here’s a sample video. You can also get coaching with a ML interviewer at FAANG companies:  https://www.datainterview.com/coaching

Tip 2 – Practice Explaining Verbally

Interviewing is not a written exercise, it’s a verbal exercise. Whether the interviewer asks you conceptual knowledge of ML, coding question, or ML system design, you will be expected to explain with clarity and in-details. As you practice interview questions, practice verbally.

Tip 3 – Join the Ultimate Prep

Get access to ML questions, cases and machine learning mock interview recordings when you join the interview program:  Join the Data Science Ultimate Prep  created by FAANG engineers/Interviewers

Elevate Your Interview Game

Essential insights and practical strategies to help you excel in machine learning interviews..

What's Inside?

• Comprehensive breakdown of the ML interview process, including all the major interview sessions: ML Fundamentals, ML Coding, ML System Design, & ML Infrastructure.
• Proven strategies for approaching and solving a wide range of ML problems, drawing from real-world scenarios.
• Step-by-step guidance on tackling ML coding challenges, system design questions, and infrastructure design problems.
• Deep dive into the mindset of interviewers, understanding what they value and how to effectively demonstrate your expertise.
• Practical examples and case studies showcasing the history of solutions to ML problems, from pioneering approaches to the state of the art.

Peng Shao has 15 years of ML leadership experience in social media, ad-tech, fintech, and e-commerce. Having interviewed nearly a thousand candidates, he has a comprehensive understanding of the skills that make a strong ML candidate. At Twitter, he served as a Staff ML Engineer, designing ML systems behind Twitter's recommendation algorithms and ads prediction. Prior to that, he co-founded a venture-backed AI startup (Roxy) which was acquired in 2019. Earlier in his career, he led ML teams at Amazon and FactSet. In these roles, he oversaw the development of ML systems including machine translation, tabular information extraction, named entity recognition, and topic modeling.

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Top 10 Data Science Case Study Interview Questions for 2024

Data Science Case Study Interview Questions and Answers to Crack Your next Data Science Interview.

According to Harvard business review, data scientist jobs have been termed “The Sexist job of the 21st century” by Harvard business review . Data science has gained widespread importance due to the availability of data in abundance. As per the below statistics, worldwide data is expected to reach 181 zettabytes by 2025

Source: statists 2021

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“Data is the new oil. It’s valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals, etc. to create a valuable entity that drives profitable activity; so must data be broken down, analyzed for it to have value.” — Clive Humby, 2006

Table of Contents

What is a data science case study, why are data scientists tested on case study-based interview questions, research about the company, ask questions, discuss assumptions and hypothesis, explaining the data science workflow, 10 data science case study interview questions and answers.

A data science case study is an in-depth, detailed examination of a particular case (or cases) within a real-world context. A data science case study is a real-world business problem that you would have worked on as a data scientist to build a machine learning or deep learning algorithm and programs to construct an optimal solution to your business problem.This would be a portfolio project for aspiring data professionals where they would have to spend at least 10-16 weeks solving real-world data science problems. Data science use cases can be found in almost every industry out there e-commerce , music streaming, stock market,.etc. The possibilities are endless. 

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A case study evaluation allows the interviewer to understand your thought process. Questions on case studies can be open-ended; hence you should be flexible enough to accept and appreciate approaches you might not have taken to solve the business problem. All interviews are different, but the below framework is applicable for most data science interviews. It can be a good starting point that will allow you to make a solid first impression in your next data science job interview. In a data science interview, you are expected to explain your data science project lifecycle , and you must choose an approach that would broadly cover all the data science lifecycle activities. The below seven steps would help you get started in the right direction. 

Source: mindsbs

Business Understanding — Explain the business problem and the objectives for the problem you solved.

Data Mining — How did you scrape the required data ? Here you can talk about the connections(can be database connections like oracle, SAP…etc.) you set up to source your data.

Data Cleaning — Explaining the data inconsistencies and how did you handle them.

Data Exploration — Talk about the exploratory data analysis you performed for the initial investigation of your data to spot patterns and anomalies.

Feature Engineering — Talk about the approach you took to select the essential features and how you derived new ones by adding more meaning to the dataset flow.

Predictive Modeling — Explain the machine learning model you trained, how did you finalized your machine learning algorithm, and talk about the evaluation techniques you performed on your accuracy score.

Data Visualization — Communicate the findings through visualization and what feedback you received.

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How to Answer Case Study-Based Data Science Interview Questions?

During the interview, you can also be asked to solve and explain open-ended, real-world case studies. This case study can be relevant to the organization you are interviewing for. The key to answering this is to have a well-defined framework in your mind that you can implement in any case study, and we uncover that framework here.

Ensure that you read about the company and its work on its official website before appearing for the data science job interview . Also, research the position you are interviewing for and understand the JD (Job description). Read about the domain and businesses they are associated with. This will give you a good idea of what questions to expect.

As case study interviews are usually open-ended, you can solve the problem in many ways. A general mistake is jumping to the answer straight away.

Try to understand the context of the business case and the key objective. Uncover the details kept intentionally hidden by the interviewer. Here is a list of questions you might ask if you are being interviewed for a financial institution -

Does the dataset include all transactions from Bank or transactions from some specific department like loans, insurance, etc.?

Is the customer data provided pre-processed, or do I need to run a statistical test to check data quality?

Which segment of borrower’s your business is targeting/focusing on? Which parameter can be used to avoid biases during loan dispersion?

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Make informed or well-thought assumptions to simplify the problem. Talk about your assumption with the interviewer and explain why you would want to make such an assumption. Try to narrow down to key objectives which you can solve. Here is a list of a few instances — 

As car sales increase consistently over time with no significant spikes, I assume seasonal changes do not impact your car sales. Hence I would prefer the modeling excluding the seasonality component.

As confirmed by you, the incoming data does not require any preprocessing. Hence I will skip the part of running statistical tests to check data quality and perform feature selection.

As IoT devices are capturing temperature data at every minute, I am required to predict weather daily. I would prefer averaging out the minute data to a day to have data daily.

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Now that you have a clear and focused objective to solve the business case. You can start leveraging the 7-step framework we briefed upon above. Think of the mining and cleaning activities that you are required to perform. Talk about feature selection and why you would prefer some features over others, and lastly, how you would select the right machine learning model for the business problem. Here is an example for car purchase prediction from auctions -

First, Prepare the relevant data by accessing the data available from various auctions. I will selectively choose the data from those auctions which are completed. At the same time, when selecting the data, I need to ensure that the data is not imbalanced.

Now I will implement feature engineering and selection to create and select relevant features like a car manufacturer, year of purchase, automatic or manual transmission…etc. I will continue this process if the results are not good on the test set.

Since this is a classification problem, I will check the prediction using the Decision trees and Random forest as this algorithm tends to do better for classification problems. If the results score is unsatisfactory, I can perform hyper parameterization to fine-tune the model and achieve better accuracy scores.

In the end, summarise the answer and explain how your solution is best suited for this business case. How the team can leverage this solution to gain more customers. For instance, building on the car sales prediction analogy, your response can be

For the car predicted as a good car during an auction, the dealers can purchase those cars and minimize the overall losses they incur upon buying a bad car. 

Often, the company you are being interviewed for would select case study questions based on a business problem they are trying to solve or have already solved. Here we list down a few case study-based data science interview questions and the approach to answering those in the interviews. Note that these case studies are often open-ended, so there is no one specific way to approach the problem statement.

1. How would you improve the bank's existing state-of-the-art credit scoring of borrowers? How will you predict someone can face financial distress in the next couple of years?

Consider the interviewer has given you access to the dataset. As explained earlier, you can think of taking the following approach. 

Ask Questions — 

Q: What parameter does the bank consider the borrowers while calculating the credit scores? Do these parameters vary among borrowers of different categories based on age group, income level, etc.?

Q: How do you define financial distress? What features are taken into consideration?

Q: Banks can lend different types of loans like car loans, personal loans, bike loans, etc.  Do you want me to focus on any one loan category?

Discuss the Assumptions  — 

As debt ratio is proportional to monthly income, we assume that people with a high debt ratio(i.e., their loan value is much higher than the monthly income) will be an outlier.

Monthly income tends to vary (mainly on the upside) over two years. Cases, where the monthly income is constant can be considered data entry issues and should not be considered for analysis. I will choose the regression model to fill up the missing values.

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Building end-to-end Data Science Workflows — 

Firstly, I will carefully select the relevant data for my analysis. I will deselect records with insane values like people with high debt ratios or inconsistent monthly income.

Identifying essential features and ensuring they do not contain missing values. If they do, fill them up. For instance, Age seems to be a necessary feature for accepting or denying a mortgage. Also, ensuring data is not imbalanced as a meager percentage of borrowers will be defaulter when compared to the complete dataset.

As this is a binary classification problem, I will start with logistic regression and slowly progress towards complex models like decision trees and random forests.

Conclude — 

Banks play a crucial role in country economies. They decide who can get finance and on what terms and can make or break investment decisions. Individuals and companies need access to credit for markets and society to function.

You can leverage this credit scoring algorithm to determine whether or not a loan should be granted by predicting the probability that somebody will experience financial distress in the next two years.

2. At an e-commerce platform, how would you classify fruits and vegetables from the image data?

Q: Do the images in the dataset contain multiple fruits and vegetables, or would each image have a single fruit or a vegetable?

Q: Can you help me understand the number of estimated classes for this classification problem?

Q: What would be an ideal dimension of an image? Do the images vary within the dataset? Are these color images or grey images?

Upon asking the above questions, let us assume the interviewer confirms that each image would contain either one fruit or one vegetable. Hence there won't be multiple classes in a single image, and our website has roughly 100 different varieties of fruits and vegetables. For simplicity, the dataset contains 50,000 images each the dimensions are 100 X 100 pixels.

Assumptions and Preprocessing—

I need to evaluate the training and testing sets. Hence I will check for any imbalance within the dataset. The number of training images for each class should be consistent. So, if there are n number of images for class A, then class B should also have n number of training images (or a variance of 5 to 10 %). Hence if we have 100 classes, the number of training images under each class should be consistent. The dataset contains 50,000 images average image per class is close to 500 images.

I will then divide the training and testing sets into 80: 20 ratios (or 70:30, whichever suits you best). I assume that the images provided might not cover all possible angles of fruits and vegetables; hence such a dataset can cause overfitting issues once the training gets completed. I will keep techniques like Data augmentation handy in case I face overfitting issues while training the model.

End to End Data Science Workflow — 

As this is a larger dataset, I would first check the availability of GPUs as processing 50,000 images would require high computation. I will use the Cuda library to move the training set to GPU for training.

I choose to develop a convolution neural network (CNN) as these networks tend to extract better features from the images when compared to the feed-forward neural network. Feature extraction is quite essential while building the deep neural network. Also, CNN requires way less computation requirement when compared to the feed-forward neural networks.

I will also consider techniques like Batch normalization and learning rate scheduling to improve the accuracy of the model and improve the overall performance of the model. If I face the overfitting issue on the validation set, I will choose techniques like dropout and color normalization to over those.

Once the model is trained, I will test it on sample test images to see its behavior. It is quite common to model that doing well on training sets does not perform well on test sets. Hence, testing the test set model is an important part of the evaluation.

The fruit classification model can be helpful to the e-commerce industry as this would help them classify the images and tag the fruit and vegetables belonging to their category.The fruit and vegetable processing industries can use the model to organize the fruits to the correct categories and accordingly instruct the device to place them on the cover belts involved in packaging and shipping to customers.

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3. How would you determine whether Netflix focuses more on TV shows or Movies?

Q: Should I include animation series and movies while doing this analysis?

Q: What is the business objective? Do you want me to analyze a particular genre like action, thriller, etc.?

Q: What is the targeted audience? Is this focus on children below a certain age or for adults?

Let us assume the interview responds by confirming that you must perform the analysis on both movies and animation data. The business intends to perform this analysis over all the genres, and the targeted audience includes both adults and children.

Assumptions — 

It would be convenient to do this analysis over geographies. As US and India are the highest content generator globally, I would prefer to restrict the initial analysis over these countries. Once the initial hypothesis is established, you can scale the model to other countries.

While analyzing movies in India, understanding the movie release over other months can be an important metric. For example, there tend to be many releases in and around the holiday season (Diwali and Christmas) around November and December which should be considered. 

End to End  Data Science Workflow — 

Firstly, we need to select only the relevant data related to movies and TV shows among the entire dataset. I would also need to ensure the completeness of the data like this has a relevant year of release, month-wise release data, Country-wise data, etc.

After preprocessing the dataset, I will do feature engineering to select the data for only those countries/geographies I am interested in. Now you can perform EDA to understand the correlation of Movies and TV shows with ratings, Categories (drama, comedies…etc.), actors…etc.

Lastly, I would focus on Recommendation clicks and revenues to understand which of the two generate the most revenues. The company would likely prefer the categories generating the highest revenue ( TV Shows vs. Movies) over others.

This analysis would help the company invest in the right venture and generate more revenue based on their customer preference. This analysis would also help understand the best or preferred categories, time in the year to release, movie directors, and actors that their customers would like to see.

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4. How would you detect fake news on social media?

Q: When you say social media, does it mean all the apps available on the internet like Facebook, Instagram, Twitter, YouTub, etc.?

Q: Does the analysis include news titles? Does the news description carry significance?

Q: As these platforms contain content from multiple languages? Should the analysis be multilingual?

Let us assume the interviewer responds by confirming that the news feeds are available only from Facebook. The new title and the news details are available in the same block and are not segregated. For simplicity, we would prefer to categorize the news available in the English language.

Assumptions and Data Preprocessing — 

I would first prefer to segregate the news title and description. The news title usually contains the key phrases and the intent behind the news. Also, it would be better to process news titles as that would require low computing than processing the whole text as a data scientist. This will lead to an efficient solution.

Also, I would also check for data imbalance. An imbalanced dataset can cause the model to be biased to a particular class. 

I would also like to take a subset of news that may focus on a specific category like sports, finance , etc. Gradually, I will increase the model scope, and this news subset would help me set up my baseline model, which can be tweaked later based on the requirement.

Firstly, it would be essential to select the data based on the chosen category. I take up sports as a category I want to start my analysis with.

I will first clean the dataset by checking for null records. Once this check is done, data formatting is required before you can feed to a natural network. I will write a function to remove characters like !”#$%&’()*+,-./:;<=>?@[]^_`{|}~ as their character does not add any value for deep neural network learning. I will also implement stopwords to remove words like ‘and’, ‘is”, etc. from the vocabulary. 

Then I will employ the NLP techniques like Bag of words or TFIDF based on the significance. The bag of words can be faster, but TF IDF can be more accurate and slower. Selecting the technique would also depend upon the business inputs.

I will now split the data in training and testing, train a machine learning model, and check the performance. Since the data set is heavy on text models like naive bayes tends to perform better in these situations.

Conclude  — 

Social media and news outlets publish fake news to increase readership or as part of psychological warfare. In general, the goal is profiting through clickbait. Clickbaits lure users and entice curiosity with flashy headlines or designs to click links to increase advertisements revenues. The trained model will help curb such news and add value to the reader's time.

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5. How would you forecast the price of a nifty 50 stock?

Q: Do you want me to forecast the nifty 50 indexes/tracker or stock price of a specific stock within nifty 50?

Q: What do you want me to forecast? Is it the opening price, closing price, VWAP, highest of the day, etc.?

Q: Do you want me to forecast daily prices /weekly/monthly prices?

Q: Can you tell me more about the historical data available? Do we have ten years or 15 years of recorded data?

With all these questions asked to the interviewer, let us assume the interviewer responds by saying that you should pick one stock among nifty 50 stocks and forecast their average price daily. The company has historical data for the last 20 years.

Assumptions and Data preprocessing — 

As we forecast the average price daily, I would consider VWAP my target or predictor value. VWAP stands for Volume Weighted Average Price, and it is a ratio of the cumulative share price to the cumulative volume traded over a given time.

Solving this data science case study requires tracking the average price over a period, and it is a classical time series problem. Hence I would refrain from using the classical regression model on the time series data as we have a separate set of machine learning models (like ARIMA , AUTO ARIMA, SARIMA…etc.) to work with such datasets.

Like any other dataset, I will first check for null and understand the % of null values. If they are significantly less, I would prefer to drop those records.

Now I will perform the exploratory data analysis to understand the average price variation from the last 20 years. This would also help me understand the tread and seasonality component of the time series data. Alternatively, I will use techniques like the Dickey-Fuller test to know if the time series is stationary or not. 

Usually, such time series is not stationary, and then I can now decompose the time series to understand the additive or multiplicative nature of time series. Now I can use the existing techniques like differencing, rolling stats, or transformation to make the time series non-stationary.

Lastly, once the time series is non-stationary, I will separate train and test data based on the dates and implement techniques like ARIMA or Facebook prophet to train the machine learning model .

Some of the major applications of such time series prediction can occur in stocks and financial trading, analyzing online and offline retail sales, and medical records such as heart rate, EKG, MRI, and ECG.

Time series datasets invoke a lot of enthusiasm between data scientists . They are many different ways to approach a Time series problem, and the process mentioned above is only one of the know techniques.

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6. How would you forecast the weekly sales of Walmart? Which department impacted most during the holidays?

Q: Walmart usually operates three different stores - supermarkets, discount stores, and neighborhood stores. Which store data shall I pick to get started with my analysis? Are the sales tracked in US dollars?

Q: How would I identify holidays in the historical data provided? Is the store closed on Black Friday week, super bowl week, or Christmas week?

Q: What are the evaluation or the loss criteria? How many departments are present across all store types?

Let us assume the interviewer responds by saying you must forecast weekly sales department-wise and not store type-wise in US dollars. You would be provided with a flag within the dataset to inform weeks having holidays. There are over 80 departments across three types of stores.

As we predict the weekly sales, I would assume weekly sales to be the target or the predictor for our data model before training.

We are tracking sales price weekly, We will use a regression model to predict our target variable, “Weekly_Sales,” a grouped/hierarchical time series. We will explore the following categories of models, engineer features, and hyper-tune parameters to choose a model with the best fit.

- Linear models

- Tree models

- Ensemble models

I will consider MEA, RMSE, and R2 as evaluation criteria.

End to End Data Science Workflow-

The foremost step is to figure out essential features within the dataset. I would explore store information regarding their size, type, and the total number of stores present within the historical dataset.

The next step would be to perform feature engineering; as we have weekly sales data available, I would prefer to extract features like ‘WeekofYear’, ‘Month’, ‘Year’, and ‘Day’. This would help the model to learn general trends.

Now I will create store and dept rank features as this is one of the end goals of the given problem. I would create these features by calculating the average weekly sales.

Now I will perform the exploratory data analysis (a.k.a EDA) to understand what story does the data has to say? I will analyze the stores and weekly dept sales for the historical data to foresee the seasonality and trends. Weekly sales against the store and weekly sales against the department to understand their significance and whether these features must be retained that will be passed to the machine learning models.

After feature engineering and selection, I will set up a baseline model and run the evaluation considering MAE, RMSE and R2. As this is a regression problem, I will begin with simple models like linear regression and SGD regressor. Later, I will move towards complex models, like Decision Trees Regressor, if the need arises. LGBM Regressor and SGB regressor.

Sales forecasting can play a significant role in the company’s success. Accurate sales forecasts allow salespeople and business leaders to make smarter decisions when setting goals, hiring, budgeting, prospecting, and other revenue-impacting factors. The solution mentioned above is one of the many ways to approach this problem statement.

With this, we come to the end of the post. But let us do a quick summary of the techniques we learned and how they can be implemented. We would also like to provide you with some practice case studies questions to help you build up your thought process for the interview.

7. Considering an organization has a high attrition rate, how would you predict if an employee is likely to leave the organization?

8. How would you identify the best cities and countries for startups in the world?

9. How would you estimate the impact on Air Quality across geographies during Covid 19?

10. A Company often faces machine failures at its factory. How would you develop a model for predictive maintenance?

Do not get intimated by the problem statement; focus on your approach -

Ask questions to get clarity

Discuss assumptions, don't assume things. Let the data tell the story or get it verified by the interviewer.

Build Workflows — Take a few minutes to put together your thoughts; start with a more straightforward approach.

Conclude — Summarize your answer and explain how it best suits the use case provided.

We hope these case study-based data scientist interview questions will give you more confidence to crack your next data science interview.

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Minimum viable study plan for machine learning interviews.

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Machine Learning Design

Getting Started

Study guide

Leetcode (not all companies ask leetcode questions).

NOTE: there are a lot of companies that do NOT ask leetcode questions. There are many paths to become an MLE, you can create your own path if you feel like leetcoding is a waste of time.

I use LC time tracking to keep track of how many times I solves a question and how long I spent each time. Once I finish non-trivial medium LC questions 3 times, I have absolutely no issues solving them in actual interviews (sometimes within 8-10 minutes). It makes a big difference. A better way is to use LeetPlug chrome extension here

Leetcode questions by categories

• Know SQL join: self join , inner, left, right etc.
• Use hackerrank to practice SQL.
• Revise/Learn SQL Window Functions: window functions

Programming

• Java garbage collection
• Python pass-by-object-reference
• Python GIL, Fluent Python, chapter 17
• Python multithread
• Python concurrency, Fluent Python, chapter 18

Statistics and probability

• The only cheatsheet that you''ll ever need

• Learn Bayesian and practice problems in Bayesian
• Let A and B be events on the same sample space, with P (A) = 0.6 and P (B) = 0.7. Can these two events be disjoint?
• Given that Alice has 2 kids, at least one of which is a girl, what is the probability that both kids are girls? (credit swierdo )
• A group of 60 students is randomly split into 3 classes of equal size. All partitions are equally likely. Jack and Jill are two students belonging to that group. What is the probability that Jack and Jill will end up in the same class?
• Given an unfair coin with the probability of heads not equal to .5. What algorithm could you use to create a list of random 1s and 0s.

Big data (NOT required for Google, Facebook interview)

• Spark architecture and Spark lessons learned (outdated since Spark 3.0 release)
• Cassandra best practice and here , link , cassandra performance
• Practice problem finding friends with MapReduce
• Everything in one page .

ML fundamentals

• Collinearity and read more
• Features scaling
• Random forest vs GBDT
• SMOTE synthetic minority over-sampling technique
• Compare discriminative vs generative model and extra read
• Logistic regression . Try to implement logistic regression from scratch. Bonus point for vectorized version in numpy + completed in 20 minutes sample code from martinpella . Followup with MapReduce version.
• Quantile regression
• L1/L2 intuition
• Decision tree and Random Forest fundamental
• Explain boosting
• Least Square as Maximum Likelihood Estimator
• Maximum Likelihood Estimator introduction
• Kmeans . Try to implement Kmeans from scratch sample code from flothesof.github.io . Bonus point for vectorized version in numpy + completed in 20 minutes. Follow-up with worst case time complexity and improvement for initialization .
• Fundamentals about PCA
• I didn't use flashcard but I'm sure it helps up to certain extend.
• Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing

DL fundamentals

• The deep learning book . Read Part ii
• Machine Learning Yearning . Read from section 5 to section 27.
• Neural network and backpropagation
• Activation functions
• Loss and optimization
• Convolution Neural network notes
• Recurrent Neural Networks

ML system design

Ml classic paper.

• Technical debt in ML
• Rules of ML
• An Opinionated Guide to ML Research . There is valuable advice in the Personal development section at the bottom.

ML productions

• Scaling ML at Uber
• DL in production

Food delivery

• Uber eats trip optimization
• Uber food discovery
• Personalized store feed
• Doordash dispatch optimization

ML design common usecases

• ML system design primer
• Video recommendation
• Feed ranking

Fraud detection (TBD)

• Ad click prediction trend
• Ad Clicks CTR
• Delayed feedbacks
• Entity embedding
• Star space, embedding all the things
• Twitter timeline ranking

Recommendations:

• Instagram explore
• TikTok recommendation
• Deep Neural Networks for YouTube Recommendations
• Wide & Deep Learning for Recommender Systems

Testimonials

• V, Amazon L5 DS

I really found the quizzes very helpful for testing my ML understanding. Also, the resources shared helped me a lot for revising concepts for my interview preparation. This course will definitely help engineers crack Machine Learning Engineering and Data Science interviews.

• K, Facebook MLE

I really like what you've built, it'll help a lot of engineers.

• D, NVIDIA DS

I have been using your github repo to prep for my interviews and got an offer with NVIDIA with their data science team. Thanks again for your help!

Woow this is very useful summaries, so nice.

• H, Microsoft

That's incredible!

The repo is extremely cohesive! Thanks again.

This repo is written based on REAL interview questions from big companies and the study materials are based on legit experts i.e Andrew Ng, Yoshua Bengio etc.

I have 6 YOE in Machine Learning and have interviewed more than dozen big companies. This is the minimum viable study plan that covers all actual interview questions from Facebook, Amazon, Apple, Google, MS, SnapChat, Linkedin etc.

If you're interested to learn more about paid ML system design course, click here . This course will provide 6-7 practical usecases with proven solutions. After this course you will be able to solve new problem with systematic approach.

Acknowledgements and contributing

Thanks for early feedbacks and contributions from Vivian , aragorn87 and others. You can create an Issue or Pull Request on this repo. You can also help upvote on ProductHunt

If you find this helpful, you can Sponsor this project. It's cool if you don't.

Thanks to this community, we have donated about $200 to HopeForPaws . If you want to support, you can contribute too on their website.

Sponsor this project

Contributors 6.

• Data Science
• Data Analysis
• Data Visualization
• Machine Learning
• Deep Learning
• Computer Vision
• Artificial Intelligence
• AI ML DS Interview Series
• AI ML DS Projects series
• Data Engineering
• Web Scrapping

Machine Learning Interview Question & Answers

Machine learning is a subfield of artificial intelligence that involves the development of algorithms and statistical models that enable computers to improve their performance in tasks through experience. So, Machine Learning is one of the booming careers in upcoming years.

If you are preparing for your next machine learning interview , this article is a one-stop destination for you. We will discuss the top 50+ most frequently asked machine learning interview questions for 2024. Our focus will be on real-life situations and questions that are commonly asked by companies like Google , Microsoft, and Amazon during their interviews.

In this article, we’ve covered a wide range of machine learning questions for both freshers and experienced individuals, ensuring thorough preparation for your next ML interview.

Table of Content

Machine Learning Interview Questions For Freshers

1. how machine learning is different from general programming, 2. what are some real-life applications of clustering algorithms, 3. how to choose an optimal number of clusters, 4. what is feature engineering how does it affect the model’s performance , 5. what is a hypothesis in machine learning, 6. how do measure the effectiveness of the clusters, 7. why do we take smaller values of the learning rate, 8. what is overfitting in machine learning and how can it be avoided, 9. why we cannot use linear regression for a classification task, 10. why do we perform normalization, 11. what is the difference between precision and recall, 12. what is the difference between upsampling and downsampling, 13. what is data leakage and how can we identify it, 14. explain the classification report and the metrics it includes., 15. what are some of the hyperparameters of the random forest regressor which help to avoid overfitting, 16. what is the bias-variance tradeoff, 17. is it always necessary to use an 80:20 ratio for the train test split, 18. what is principal component analysis, 19. what is one-shot learning, 20. what is the difference between manhattan distance and euclidean distance, 21. what is the difference between covariance and correlation, 22. what is the difference between one hot encoding and ordinal encoding, 23. how to identify whether the model has overfitted the training data or not, 24. how can you conclude about the model’s performance using the confusion matrix, 25. what is the use of the violin plot, 26. what are the five statistical measures represented in a boxplot, 27. what is the difference between stochastic gradient descent (sgd) and gradient descent (gd), 28. what is the central limit theorem, advanced machine learning interview questions, 29. explain the working principle of svm., 30. what is the difference between the k-means and k-means++ algorithms, 31. explain some measures of similarity which are generally used in machine learning., 32. what happens to the mean, median, and mode when your data distribution is right skewed and left skewed, 33. whether decision tree or random forest is more robust to the outliers., 34. what is the difference between l1 and l2 regularization what is their significance, 35. what is a radial basis function explain its use., 36. explain smote method used to handle data imbalance., 37. does the accuracy score always a good metric to measure the performance of a classification model, 38. what is knn imputer, 39. explain the working procedure of the xgb model., 40. what is the purpose of splitting a given dataset into training and validation data, 41. explain some methods to handle missing values in that data., 42. what is the difference between k-means and the knn algorithm, 43. what is linear discriminant analysis, 44. how can we visualize high-dimensional data in 2-d, 45. what is the reason behind the curse of dimensionality, 46. whether the metric mae or mse or rmse is more robust to the outliers., 47. why removing highly correlated features are considered a good practice, 48. what is the difference between the content-based and collaborative filtering algorithms of recommendation systems.

This ML Questions is also beneficial for individuals who are looking for a quick revision of their machine-learning concepts.

In general programming, we have the data and the logic by using these two we create the answers. But in machine learning, we have the data and the answers and we let the machine learn the logic from them so, that the same logic can be used to answer the questions which will be faced in the future.

Also, there are times when writing logic in codes is not possible so, at those times machine learning becomes a saviour and learns the logic itself.

The clustering technique can be used in multiple domains of data science like image classification, customer segmentation, and recommendation engine. One of the most common use is in market research and customer segmentation which is then utilized to target a particular market group to expand the businesses and profitable outcomes. 

By using the Elbow method we decide an optimal number of clusters that our clustering algorithm must try to form. The main principle behind this method is that if we will increase the number of clusters the error value will decrease.

But after an optimal number of features, the decrease in the error value is insignificant so, at the point after which this starts to happen, we choose that point as the optimal number of clusters that the algorithm will try to form.

ELBOW METHOD

The optimal number of clusters from the above figure is 3.

Feature engineering refers to developing some new features by using existing features. Sometimes there is a very subtle mathematical relation between some features which if explored properly then the new features can be developed using those mathematical operations.

Also, there are times when multiple pieces of information are clubbed and provided as a single data column. At those times developing new features and using them help us to gain deeper insights into the data as well as if the features derived are significant enough helps to improve the model’s performance a lot.

A hypothesis is a term that is generally used in the Supervised machine learning domain. As we have independent features and target variables and we try to find an approximate function mapping from the feature space to the target variable that approximation of mapping is known as a hypothesis . 

There are metrics like Inertia or Sum of Squared Errors (SSE), Silhouette Score, l1, and l2 scores. Out of all of these metrics, the Inertia or Sum of Squared Errors (SSE) and Silhouette score is a common metrics for measuring the effectiveness of the clusters.

Although this method is quite expensive in terms of computation cost. The score is high if the clusters formed are dense and well separated.

Smaller values of learning rate help the training process to converge more slowly and gradually toward the global optimum instead of fluctuating around it. This is because a smaller learning rate results in smaller updates to the model weights at each iteration, which can help to ensure that the updates are more precise and stable. If the learning rate is too large, the model weights can update too quickly, which can cause the training process to overshoot the global optimum and miss it entirely.

So, to avoid this oscillation of the error value and achieve the best weights for the model this is necessary to use smaller values of the learning rate.

Overfitting happens when the model learns patterns as well as the noises present in the data this leads to high performance on the training data but very low performance for data that the model has not seen earlier. To avoid overfitting there are multiple methods that we can use:

• Early stopping of the model’s training in case of validation training stops increasing but the training keeps going on.
• Using regularization methods like L1 or L2 regularization which is used to penalize the model’s weights to avoid overfitting .

The main reason why we cannot use linear regression for a classification task is that the output of linear regression is continuous and unbounded, while classification requires discrete and bounded output values. 

If we use linear regression for the classification task the error function graph will not be convex. A convex graph has only one minimum which is also known as the global minima but in the case of the non-convex graph, there are chances of our model getting stuck at some local minima which may not be the global minima. To avoid this situation of getting stuck at the local minima we do not use the linear regression algorithm for a classification task.

To achieve stable and fast training of the model we use normalization techniques to bring all the features to a certain scale or range of values. If we do not perform normalization then there are chances that the gradient will not converge to the global or local minima and end up oscillating back and forth. Read more about it here .

Precision is simply the ratio between the true positives(TP) and all the positive examples (TP+FP) predicted by the model. In other words, precision measures how many of the predicted positive examples are actually true positives. It is a measure of the model’s ability to avoid false positives and make accurate positive predictions.

[Tex]\text{Precision}=\frac{TP}{TP\; +\; FP}[/Tex]

But in the case of a recall, we calculate the ratio of true positives (TP) and the total number of examples (TP+FN) that actually fall in the positive class. recall measures how many of the actual positive examples are correctly identified by the model. It is a measure of the model’s ability to avoid false negatives and identify all positive examples correctly.

[Tex]\text{Recall}=\frac{TP}{TP\; +\; FN}[/Tex]

In the upsampling method, we increase the number of samples in the minority class by randomly selecting some points from the minority class and adding them to the dataset repeat this process till the dataset gets balanced for each class. But here is a disadvantage the training accuracy becomes high as in each epoch model trained more than once in each epoch but the same high accuracy is not observed in the validation accuracy. 

In the case of downsampling, we decrease the number of samples in the majority class by selecting some random number of points that are equal to the number of data points in the minority class so that the distribution becomes balanced. In this case, we have to suffer from data loss which may lead to the loss of some critical information as well. 

If there is a high correlation between the target variable and the input features then this situation is referred to as data leakage. This is because when we train our model with that highly correlated feature then the model gets most of the target variable’s information in the training process only and it has to do very little to achieve high accuracy. In this situation, the model gives pretty decent performance both on the training as well as the validation data but as we use that model to make actual predictions then the model’s performance is not up to the mark. This is how we can identify data leakage.

Classification reports are evaluated using classification metrics that have precision, recall, and f1-score on a per-class basis.

• Precision can be defined as the ability of a classifier not to label an instance positive that is actually negative. 
• Recall is the ability of a classifier to find all positive values. For each class, it is defined as the ratio of true positives to the sum of true positives and false negatives. 
• F1-score is a harmonic mean of precision and recall. 
• Support is the number of samples used for each class.
• The overall accuracy score of the model is also there to get a high-level review of the performance. It is the ratio between the total number of correct predictions and the total number of datasets.
• Macro avg is nothing but the average of the metric(precision, recall, f1-score) values for each class. 
• The weighted average is calculated by providing a higher preference to that class that was present in the higher number in the datasets.

The most important hyper-parameters of a Random Forest are:

• max_depth – Sometimes the larger depth of the tree can create overfitting. To overcome it, the depth should be limited.
• n-estimator – It is the number of decision trees we want in our forest.
• min_sample_split – It is the minimum number of samples an internal node must hold in order to split into further nodes.
• max_leaf_nodes – It helps the model to control the splitting of the nodes and in turn, the depth of the model is also restricted.

First, let’s understand what is bias and variance :

• Bias refers to the difference between the actual values and the predicted values by the model. Low bias means the model has learned the pattern in the data and high bias means the model is unable to learn the patterns present in the data i.e the underfitting.
• Variance refers to the change in accuracy of the model’s prediction on which the model has not been trained. Low variance is a good case but high variance means that the performance of the training data and the validation data vary a lot.

If the bias is too low but the variance is too high then that case is known as overfitting. So, finding a balance between these two situations is known as the bias-variance trade-off.

No there is no such necessary condition that the data must be split into 80:20 ratio. The main purpose of the splitting is to have some data which the model has not seen previously so, that we can evaluate the performance of the model.

If the dataset contains let’s say 50,000 rows of data then only 1000 or maybe 2000 rows of data is enough to evaluate the model’s performance.

PCA(Principal Component Analysis) is an unsupervised machine learning dimensionality reduction technique in which we trade off some information or patterns of the data at the cost of reducing its size significantly. In this algorithm, we try to preserve the variance of the original dataset up to a great extent let’s say 95%. For very high dimensional data sometimes even at the loss of 1% of the variance, we can reduce the data size significantly.

By using this algorithm we can perform image compression, visualize high-dimensional data as well as make data visualization easy.

One-shot learning is a concept in machine learning where the model is trained to recognize the patterns in datasets from a single example instead of training on large datasets. This is useful when we haven’t large datasets. It is applied to find the similarity and dissimilarities between the two images.

Both Manhattan Distance and Euclidean distance are two distance measurement techniques. 

Manhattan Distance (MD) is calculated as the sum of absolute differences between the coordinates of two points along each dimension. 

[Tex]MD = \left| x_1 – x_2\right| +  \left| y_1-y_2\right|[/Tex]

Euclidean Distance (ED) is calculated as the square root of the sum of squared differences between the coordinates of two points along each dimension.

[Tex]ED = \sqrt{\left ( x_1 – x_2 \right )^2 + \left ( y_1-y_2 \right )^2}[/Tex]

Generally, these two metrics are used to evaluate the effectiveness of the clusters formed by a clustering algorithm.

As the name suggests, Covariance provides us with a measure of the extent to which two variables differ from each other. But on the other hand, correlation gives us the measure of the extent to which the two variables are related to each other. Covariance can take on any value while correlation is always between -1 and 1. These measures are used during the exploratory data analysis to gain insights from the data.

One Hot encoding and ordinal encoding both are different methods to convert categorical features to numeric ones the difference is in the way they are implemented. In one hot encoding, we create a separate column for each category and add 0 or 1 as per the value corresponding to that row. Contrary to one hot encoding, In ordinal encoding, we replace the categories with numbers from 0 to n-1 based on the order or rank where n is the number of unique categories present in the dataset. The main difference between one-hot encoding and ordinal encoding is that one-hot encoding results in a binary matrix representation of the data in the form of 0 and 1, it is used when there is no order or ranking between the dataset whereas ordinal encoding represents categories as ordinal values.

This is the step where the splitting of the data into training and validation data proves to be a boon. If the model’s performance on the training data is very high as compared to the performance on the validation data then we can say that the model has overfitted the training data by learning the patterns as well as the noise present in the dataset.

confusion matrix summarizes the performance of a classification model. In a confusion matrix, we get four types of output (in case of a binary classification problem) which are TP, TN, FP, and FN. As we know that there are two diagonals possible in a square, and one of these two diagonals represents the numbers for which our model’s prediction and the true labels are the same. Our target is also to maximize the values along these diagonals. From the confusion matrix, we can calculate various evaluation metrics like accuracy, precision, recall, F1 score, etc.

The name violin plot has been derived from the shape of the graph which matches the violin. This graph is an extension of the Kernel Density Plot along with the properties of the boxplot. All the statistical measures shown by a boxplot are also shown by the violin plot but along with this, The width of the violin represents the density of the variable in the different regions of values. This visualization tool is generally used in the exploratory data analysis step to check the distribution of the continuous data variables. 

With this, we have covered some of the most important Machine Learning concepts which are generally asked by the interviewers to test the technical understanding of a candidate also, we would like to wish you all the best for your next interview.

Boxplot with its statistical measures

• IQR = Q3-Q1
• Left Whisker = Q1-1.5*IQR
• Q1 – This is also known as the 25 percentile.
• Q2 – This is the median of the data or 50 percentile.
• Q3 – This is also known as 75 percentile
• Right Whisker = Q3 + 1.5*IQR

In the gradient descent algorithm train our model on the whole dataset at once. But in Stochastic Gradient Descent, the model is trained by using a mini-batch of training data at once. If we are using SGD then one cannot expect the training error to go down smoothly. The training error oscillates but after some training steps, we can say that the training error has gone down. Also, the minima achieved by using GD may vary from that achieved using the SGD. It is observed that the minima achieved by using SGD are close to GD but not the same. 

This theorem is related to sampling statistics and its distribution. As per this theorem the sampling distribution of the sample means tends to towards a normal distribution as the sample size increases. No matter how the population distribution is shaped. i.e if we take some sample points from the distribution and calculate its mean then the distribution of those mean points will follow a normal/gaussian distribution no matter from which distribution we have taken the sample points.

There is one condition that the size of the sample must be greater than or equal to 30 for the CLT to hold. and the mean of the sample means approaches the population mean.

Machine Learning Interview Questions 2024

A data set that is not separable in different classes in one plane may be separable in another plane. This is exactly the idea behind the SVM in this a low dimensional data is mapped to high dimensional data so, that it becomes separable in the different classes. A hyperplane is determined after mapping the data into a higher dimension which can separate the data into categories. SVM model can even learn non-linear boundaries with the objective that there should be as much margin as possible between the categories in which the data has been categorized. To perform this mapping different types of kernels are used like radial basis kernel, gaussian kernel, polynomial kernel, and many others.

The only difference between the two is in the way centroids are initialized. In the k-means algorithm, the centroids are initialized randomly from the given points. There is a drawback in this method that sometimes this random initialization leads to non-optimized clusters due to maybe initialization of two clusters close to each other. 

To overcome this problem k-means++ algorithm was formed. In k-means++, The first centroid is selected randomly from the data points. The selection of subsequent centroids is based on their separation from the initial centroids. The probability of a point being selected as the next centroid is proportional to the squared distance between the point and the closest centroid that has already been selected. This guarantees that the centroids are evenly spread apart and lowers the possibility of convergence to less-than-ideal clusters. This helps the algorithm reach the global minima instead of getting stuck at some local minima. Read more about it here .

Some of the most commonly used similarity measures are as follows:

• Cosine Similarity – By considering the two vectors in n – dimension we evaluate the cosine of the angle between the two. The range of this similarity measure varies from [-1, 1] where the value 1 represents that the two vectors are highly similar and -1 represents that the two vectors are completely different from each other.
• Euclidean or Manhattan Distance – These two values represent the distances between the two points in an n-dimensional plane. The only difference between the two is in the way the two are calculated.
• Jaccard Similarity – It is also known as IoU or Intersection over union it is widely used in the field of object detection to evaluate the overlap between the predicted bounding box and the ground truth bounding box.

In the case of a right-skewed distribution also known as a positively skewed distribution mean is greater than the median which is greater than the mode. But in the case of left-skewed distribution, the scenario is completely reversed.

Right Skewed Distribution

Mode < Median < Mean

Left Skewed Distribution,

Mean <Median < Mode

Left Skewed Distribution

Decision trees and random forests are both relatively robust to outliers. A random forest model is an ensemble of multiple decision trees so, the output of a random forest model is an aggregate of multiple decision trees.

So, when we average the results the chances of overfitting get reduced. Hence we can say that the random forest models are more robust to outliers.

L1 regularization : In L1 regularization also known as Lasso regularization in which we add the sum of absolute values of the weights of the model in the loss function. In L1 regularization weights for those features which are not at all important are penalized to zero so, in turn, we obtain feature selection by using the L1 regularization technique.

L2 regularization : In L2 regularization also known as Ridge regularization in which we add the square of the weights to the loss function. In both of these regularization methods, weights are penalized but there is a subtle difference between the objective they help to achieve. 

In L2 regularization the weights are not penalized to 0 but they are near zero for irrelevant features. It is often used to prevent overfitting by shrinking the weights towards zero, especially when there are many features and the data is noisy.

RBF (radial basis function) is a real-valued function used in machine learning whose value only depends upon the input and fixed point called the center. The formula for the radial basis function is as follows:

[Tex]K\left ( x,\; {x}^{‘}\right )=exp\left ( -\frac{\left\|x-{x}^{‘} \right\|^2}{2\sigma ^2} \right )[/Tex]

Machine learning systems frequently use the RBF function for a variety of functions, including:

• RBF networks can be used to approximate complex functions. By training the network’s weights to suit a set of input-output pairs, 
• RBF networks can be used for unsupervised learning to locate data groups. By treating the RBF centers as cluster centers,
• RBF networks can be used for classification tasks by training the network’s weights to divide inputs into groups based on how far from the RBF nodes they are.

It is one of the very famous kernels which is generally used in the SVM algorithm to map low dimensional data to a higher dimensional plane so, we can determine a boundary that can separate the classes in different regions of those planes with as much margin as possible. 

The synthetic Minority Oversampling Technique is one of the methods which is used to handle the data imbalance problem in the dataset. In this method, we synthesized new data points using the existing ones from the minority classes by using linear interpolation. The advantage of using this method is that the model does not get trained on the same data. But the disadvantage of using this method is that it adds undesired noise to the dataset and can lead to a negative effect on the model’s performance.

No, there are times when we train our model on an imbalanced dataset the accuracy score is not a good metric to measure the performance of the model. In such cases, we use precision and recall to measure the performance of a classification model. Also, f1-score is another metric that can be used to measure performance but in the end, f1-score is also calculated using precision and recall as the f1-score is nothing but the harmonic mean of the precision and recall. 

We generally impute null values by the descriptive statistical measures of the data like mean, mode, or median but KNN Imputer is a more sophisticated method to fill the null values. A distance parameter is also used in this method which is also known as the k parameter. The work is somehow similar to the clustering algorithm. The missing value is imputed in reference to the neighborhood points of the missing values.

XGB model is an example of the ensemble technique of machine learning in this method weights are optimized in a sequential manner by passing them to the decision trees. After each pass, the weights become better and better as each tree tries to optimize the weights, and finally, we obtain the best weights for the problem at hand. Techniques like regularized gradient and mini-batch gradient descent have been used to implement this algorithm so, that it works in a very fast and optimized manner.

The main purpose is to keep some data left over on which the model has not been trained so, that we can evaluate the performance of our machine learning model after training. Also, sometimes we use the validation dataset to choose among the multiple state-of-the-art machine learning models. Like we first train some models let’s say LogisticRegression, XGBoost, or any other than test their performance using validation data and choose the model which has less difference between the validation and the training accuracy.

Some of the methods to handle missing values are as follows:

• Removing the rows with null values may lead to the loss of some important information.
• Removing the column having null values if it has very less valuable information. it may lead to the loss of some important information.
• Imputing null values with descriptive statistical measures like mean, mode, and median.
• Using methods like KNN Imputer to impute the null values in a more sophisticated way.

k-means algorithm is one of the popular unsupervised machine learning algorithms which is used for clustering purposes. But the KNN is a model which is generally used for the classification task and is a supervised machine learning algorithm. The k-means algorithm helps us to label the data by forming clusters within the dataset.

LDA is a supervised machine learning dimensionality reduction technique because it uses target variables also for dimensionality reduction. It is commonly used for classification problems. The LDA mainly works on two objectives:

• Maximize the distance between the means of the two classes.
• Minimize the variation within each class.

One of the most common and effective methods is by using the t-SNE algorithm which is a short form for t-Distributed Stochastic Neighbor Embedding. This algorithm uses some non-linear complex methods to reduce the dimensionality of the given data. We can also use PCA or LDA to convert n-dimensional data to 2 – dimensional so, that we can plot it to get visuals for better analysis. But the difference between the PCA and t-SNE is that the former tries to preserve the variance of the dataset but the t-SNE tries to preserve the local similarities in the dataset.

As the dimensionality of the input data increases the amount of data required to generalize or learn the patterns present in the data increases. For the model, it becomes difficult to identify the pattern for every feature from the limited number of datasets or we can say that the weights are not optimized properly due to the high dimensionality of the data and the limited number of examples used to train the model. Due to this after a certain threshold for the dimensionality of the input data, we have to face the curse of dimensionality.

Out of the above three metrics, MAE is robust to the outliers as compared to the MSE or RMSE. The main reason behind this is because of Squaring the error values. In the case of an outlier, the error value is already high and then we squared it which results in an explosion in the error values more than expected and creates misleading results for the gradient.

When two features are highly correlated, they may provide similar information to the model, which may cause overfitting. If there are highly correlated features in the dataset then they unnecessarily increase the dimensionality of the feature space and sometimes create the problem of the curse of dimensionality. If the dimensionality of the feature space is high then the model training may take more time than expected, it will increase the complexity of the model and chances of error. This somehow also helps us to achieve data compression as the features have been removed without much loss of data.

In a content-based recommendation system, similarities in the content and services are evaluated, and then by using these similarity measures from past data we recommend products to the user. But on the other hand in collaborative filtering, we recommend content and services based on the preferences of similar users. For example, if one user has taken A and B services in past and a new user has taken service A then service A will be recommended to him based on the other user’s preferences.

Machine learning is a rapidly advancing field with new concepts constantly emerging. To stay up to date, join communities, attend conferences, and read research papers. By doing so, you can enhance your understanding and effectively tackle machine learning interviews. Continuous learning and active involvement are key to success in this dynamic field.

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Data science case interviews (what to expect & how to prepare)

Data science case studies are tough to crack: they’re open-ended, technical, and specific to the company. Interviewers use them to test your ability to break down complex problems and your use of analytical thinking to address business concerns.

So we’ve put together this guide to help you familiarize yourself with case studies at companies like Amazon, Google, and Meta (Facebook), as well as how to prepare for them, using practice questions and a repeatable answer framework.

Here’s the first thing you need to know about tackling data science case studies: always start by asking clarifying questions, before jumping in to your plan.

Let’s get started.

• What to expect in data science case study interviews
• How to approach data science case studies
• Sample cases from FAANG data science interviews
• How to prepare for data science case interviews

Click here to practice 1-on-1 with ex-FAANG interviewers

1. what to expect in data science case study interviews.

Before we get into an answer method and practice questions for data science case studies, let’s take a look at what you can expect in this type of interview.

Of course, the exact interview process for data scientist candidates will depend on the company you’re applying to, but case studies generally appear in both the pre-onsite phone screens and during the final onsite or virtual loop.

These questions may take anywhere from 10 to 40 minutes to answer, depending on the depth and complexity that the interviewer is looking for. During the initial phone screens, the case studies are typically shorter and interspersed with other technical and/or behavioral questions. During the final rounds, they will likely take longer to answer and require a more detailed analysis.

While some candidates may have the opportunity to prepare in advance and present their conclusions during an interview round, most candidates work with the information the interviewer offers on the spot.

1.1 The types of data science case studies

Generally, there are two types of case studies:

• Analysis cases , which focus on how you translate user behavior into ideas and insights using data. These typically center around a product, feature, or business concern that’s unique to the company you’re interviewing with.
• Modeling cases , which are more overtly technical and focus on how you build and use machine learning and statistical models to address business problems.

The number of case studies that you’ll receive in each category will depend on the company and the position that you’ve applied for. Facebook , for instance, typically doesn’t give many machine learning modeling cases, whereas Amazon does.

Also, some companies break these larger groups into smaller subcategories. For example, Facebook divides its analysis cases into two types: product interpretation and applied data . 

You may also receive in-depth questions similar to case studies, which test your technical capabilities (e.g. coding, SQL), so if you’d like to learn more about how to answer coding interview questions, take a look here .

We’ll give you a step-by-step method that can be used to answer analysis and modeling cases in section 2 . But first, let’s look at how interviewers will assess your answers.

1.2 What interviewers are looking for

We’ve researched accounts from ex-interviewers and data scientists to pinpoint the main criteria that interviewers look for in your answers. While the exact grading rubric will vary per company, this list from an ex-Google data scientist is a good overview of the biggest assessment areas:

• Structure : candidate can break down an ambiguous problem into clear steps
• Completeness : candidate is able to fully answer the question
• Soundness : candidate’s solution is feasible and logical
• Clarity : candidate’s explanations and methodology are easy to understand
• Speed : candidate manages time well and is able to come up with solutions quickly

You’ll be able to improve your skills in each of these categories by practicing data science case studies on your own, and by working with an answer framework. We’ll get into that next.

2. How to approach data science case studies

Approaching data science cases with a repeatable framework will not only add structure to your answer, but also help you manage your time and think clearly under the stress of interview conditions.

Let’s go over a framework that you can use in your interviews, then break it down with an example answer.

2.1 Data science case framework: CAPER

We've researched popular frameworks used by real data scientists, and consolidated them to be as memorable and useful in an interview setting as possible.

Try using the framework below to structure your thinking during the interview. 

• Clarify : Start by asking questions. Case questions are ambiguous, so you’ll need to gather more information from the interviewer, while eliminating irrelevant data. The types of questions you’ll ask will depend on the case, but consider: what is the business objective? What data can I access? Should I focus on all customers or just in X region?
• Assume : Narrow the problem down by making assumptions and stating them to the interviewer for confirmation. (E.g. the statistical significance is X%, users are segmented based on XYZ, etc.) By the end of this step you should have constrained the problem into a clear goal.
• Plan : Now, begin to craft your solution. Take time to outline a plan, breaking it into manageable tasks. Once you’ve made your plan, explain each step that you will take to the interviewer, and ask if it sounds good to them.
• Execute : Carry out your plan, walking through each step with the interviewer. Depending on the type of case, you may have to prepare and engineer data, code, apply statistical algorithms, build a model, etc. In the majority of cases, you will need to end with business analysis.
• Review : Finally, tie your final solution back to the business objectives you and the interviewer had initially identified. Evaluate your solution, and whether there are any steps you could have added or removed to improve it. 

Now that you’ve seen the framework, let’s take a look at how to implement it.

2.2 Sample answer using the CAPER framework

Below you’ll find an answer to a Facebook data science interview question from the Applied Data loop. This is an example that comes from Facebook’s data science interview prep materials, which you can find here .

Try this question:

Imagine that Facebook is building a product around high schools, starting with about 300 million users who have filled out a field with the name of their current high school. How would you find out how much of this data is real?

First, we need to clarify the question, eliminating irrelevant data and pinpointing what is the most important. For example:

• What exactly does “real” mean in this context?
• Should we focus on whether the high school itself is real, or whether the user actually attended the high school they’ve named?

After discussing with the interviewer, we’ve decided to focus on whether the high school itself is real first, followed by whether the user actually attended the high school they’ve named.

Next, we’ll narrow the problem down and state our assumptions to the interviewer for confirmation. Here are some assumptions we could make in the context of this problem:

• The 300 million users are likely teenagers, given that they’re listing their current high school
• We can assume that a high school that is listed too few times is likely fake
• We can assume that a high school that is listed too many times (e.g. 10,000+ students) is likely fake

The interviewer has agreed with each of these assumptions, so we can now move on to the plan.

Next, it’s time to make a list of actionable steps and lay them out for the interviewer before moving on.

First, there are two approaches that we can identify:

• A high precision approach, which provides a list of people who definitely went to a confirmed high school
• A high recall approach, more similar to market sizing, which would provide a ballpark figure of people who went to a confirmed high school

As this is for a product that Facebook is currently building, the product use case likely calls for an estimate that is as accurate as possible. So we can go for the first approach, which will provide a more precise estimate of confirmed users listing a real high school. 

Now, we list the steps that make up this approach:

• To find whether a high school is real: Draw a distribution with the number of students on the X axis, and the number of high schools on the Y axis, in order to find and eliminate the lower and upper bounds
• To find whether a student really went to a high school: use a user’s friend graph and location to determine the plausibility of the high school they’ve named

The interviewer has approved the plan, which means that it’s time to execute.

4. Execute 

Step 1: Determining whether a high school is real

Going off of our plan, we’ll first start with the distribution.

We can use x1 to denote the lower bound, below which the number of times a high school is listed would be too small for a plausible school. x2 then denotes the upper bound, above which the high school has been listed too many times for a plausible school.

Here is what that would look like:

Be prepared to answer follow up questions. In this case, the interviewer may ask, “looking at this graph, what do you think x1 and x2 would be?”

Based on this distribution, we could say that x1 is approximately the 5th percentile, or somewhere around 100 students. So, out of 300 million students, if fewer than 100 students list “Applebee” high school, then this is most likely not a real high school.

x2 is likely around the 95th percentile, or potentially as high as the 99th percentile. Based on intuition, we could estimate that number around 10,000. So, if more than 10,000 students list “Applebee” high school, then this is most likely not real. Here is how that looks on the distribution:

At this point, the interviewer may ask more follow-up questions, such as “how do we account for different high schools that share the same name?”

In this case, we could group by the schools’ name and location, rather than name alone. If the high school does not have a dedicated page that lists its location, we could deduce its location based on the city of the user that lists it. 

Step 2: Determining whether a user went to the high school

A strong signal as to whether a user attended a specific high school would be their friend graph: a set number of friends would have to have listed the same current high school. For now, we’ll set that number at five friends.

Don’t forget to call out trade-offs and edge cases as you go. In this case, there could be a student who has recently moved, and so the high school they’ve listed does not reflect their actual current high school. 

To solve this, we could rely on users to update their location to reflect the change. If users do not update their location and high school, this would present an edge case that we would need to work out later.

To conclude, we could use the data from both the friend graph and the initial distribution to confirm the two signifiers: a high school is real, and the user really went there.

If enough users in the same location list the same high school, then it is likely that the high school is real, and that the users really attend it. If there are not enough users in the same location that list the same high school, then it is likely that the high school is not real, and the users do not actually attend it.

3. Sample cases from FAANG data science interviews

Having worked through the sample problem above, try out the different kinds of case studies that have been asked in data science interviews at FAANG companies. We’ve divided the questions into types of cases, as well as by company.

For more information about each of these companies’ data science interviews, take a look at these guides:

• Facebook data scientist interview guide
• Amazon data scientist interview guide
• Google data scientist interview guide

Now let’s get into the questions. This is a selection of real data scientist interview questions, according to data from Glassdoor.

Data science case studies

Facebook - Analysis (product interpretation)

• How would you measure the success of a product?
• What KPIs would you use to measure the success of the newsfeed?
• Friends acceptance rate decreases 15% after a new notifications system is launched - how would you investigate?

Facebook - Analysis (applied data)

• How would you evaluate the impact for teenagers when their parents join Facebook?
• How would you decide to launch or not if engagement within a specific cohort decreased while all the rest increased?
• How would you set up an experiment to understand feature change in Instagram stories?

Amazon - modeling

• How would you improve a classification model that suffers from low precision?
• When you have time series data by month, and it has large data records, how will you find significant differences between this month and previous month?

Google - Analysis

• You have a google app and you make a change. How do you test if a metric has increased or not?
• How do you detect viruses or inappropriate content on YouTube?
• How would you compare if upgrading the android system produces more searches?

4. How to prepare for data science case interviews

Understanding the process and learning a method for data science cases will go a long way in helping you prepare. But this information is not enough to land you a data science job offer. 

To succeed in your data scientist case interviews, you're also going to need to practice under realistic interview conditions so that you'll be ready to perform when it counts. 

For more information on how to prepare for data science interviews as a whole, take a look at our guide on data science interview prep .

4.1 Practice on your own

Start by answering practice questions alone. You can use the list in section 3 , and interview yourself out loud. This may sound strange, but it will significantly improve the way you communicate your answers during an interview. 

Play the role of both the candidate and the interviewer, asking questions and answering them, just like two people would in an interview. This will help you get used to the answer framework and get used to answering data science cases in a structured way.

4.2 Practice with peers

Once you’re used to answering questions on your own , then a great next step is to do mock interviews with friends or peers. This will help you adapt your approach to accommodate for follow-ups and answer questions you haven’t already worked through.

This can be especially helpful if your friend has experience with data scientist interviews, or is at least familiar with the process.

4.3 Practice with ex-interviewers

Finally, you should also try to practice data science mock interviews with expert ex-interviewers, as they’ll be able to give you much more accurate feedback than friends and peers.

If you know a data scientist or someone who has experience running interviews at a big tech company, then that's fantastic. But for most of us, it's tough to find the right connections to make this happen. And it might also be difficult to practice multiple hours with that person unless you know them really well.

Here's the good news. We've already made the connections for you. We’ve created a coaching service where you can practice 1-on-1 with ex-interviewers from leading tech companies. Learn more and start scheduling sessions today .

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The Business Impact of Machine Learning: Real-world Case Studies

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Title: flight delay prediction using hybrid machine learning approach: a case study of major airlines in the united states.

Abstract: The aviation industry has experienced constant growth in air traffic since the deregulation of the U.S. airline industry in 1978. As a result, flight delays have become a major concern for airlines and passengers, leading to significant research on factors affecting flight delays such as departure, arrival, and total delays. Flight delays result in increased consumption of limited resources such as fuel, labor, and capital, and are expected to increase in the coming decades. To address the flight delay problem, this research proposes a hybrid approach that combines the feature of deep learning and classic machine learning techniques. In addition, several machine learning algorithms are applied on flight data to validate the results of proposed model. To measure the performance of the model, accuracy, precision, recall, and F1-score are calculated, and ROC and AUC curves are generated. The study also includes an extensive analysis of the flight data and each model to obtain insightful results for U.S. airlines.

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Deep learning case study interview

Many accomplished students and newly minted AI professionals ask us$:$ How can I prepare for interviews? Good recruiters try setting up job applicants for success in interviews, but it may not be obvious how to prepare for them. We interviewed over 100 leaders in machine learning and data science to understand what AI interviews are and how to prepare for them.

TABLE OF CONTENTS

• I What to expect in the deep learning case study interview
• II Recommended framework
• III Interview tips
• IV Resources

AI organizations divide their work into data engineering, modeling, deployment, business analysis, and AI infrastructure. The necessary skills to carry out these tasks are a combination of technical, behavioral, and decision making skills. Deep learning skills are sometimes required, especially in organizations focusing on computer vision, natural language processing, or speech recognition.

The deep learning case study interview focuses on technical and decision making skills, and you’ll encounter it during an onsite round for a Deep Learning Engineer (DLE), Deep Learning Researcher (DLR), or Software Engineer-Deep Learning (SE-DL) role. You can learn more about these roles in our AI Career Pathways report and about other types of interviews in The Skills Boost .

I   What to expect in the deep learning case study interview

The interviewer is evaluating your approach to a real-world deep learning problem. The interview is usually a technical discussion on an open-ended question. There is no exact solution to the question; it’s your thought process that the interviewer is evaluating. Here’s a list of interview questions you might be asked:

• How would you build a speech recognition system powering a virtual assistant like Amazon Alexa, Google Home, Apple Siri, and Baidu’s DuerOS?
• As a deep learning engineer, you are asked to build an object detector for a zoo. How would you get started?
• How would you build an algorithm that auto-completes your sentence when writing an email?
• In your opinion, what are technical challenges related to the deployment of an autonomous vehicle in a geofenced area?
• You built a computer vision algorithm that can detect pneumonia from chest X-rays. How would you convince a radiologist to use it?
• You are tackling the school dropout problem. How would you build a model that can determine whether a student is at-risk or not, and plan an intervention?

II   Recommended framework

All interviews are different, but the ASPER framework is applicable to a variety of case studies:

• Ask . Ask questions to uncover details that were kept hidden by the interviewer. Specifically, you want to answer the following questions: “what are the product requirements and evaluation metrics?”, “what data do I have access to?”, ”how much time and computational resources do I have to run experiments?”, ”how will the learning algorithm be used at test time, and does it need to be regularly re-trained?”.
• Suppose . Make justified assumptions to simplify the problem. Examples of assumptions are: “we are in small data regime”, “the data distribution won’t change over time”, “our model performs better than humans”, “labels are reliable”, etc.
• Plan . Break down the problem into tasks. A common task sequence in the deep learning case study interview is: (i) data engineering, (ii) modeling, and (iii) deployment.
• Execute . Announce your plan, and tackle the tasks one by one. In this step, the interviewer might ask you to write code or explain the maths behind your proposed method.
• Recap . At the end of the interview, summarize your answer and mention the tools and frameworks you would use to perform the work. It is also a good time to express your ideas on how the problem can be extended.

III   Interview tips

Every interview is an opportunity to show your skills and motivation for the role. Thus, it is important to prepare in advance. Here are useful rules of thumb to follow:

Show your motivation.

In deep learning case study interviews, the interviewer will evaluate your excitement for the company’s product. Make sure to show your curiosity, creativity and enthusiasm.

Listen to the hints given by your interviewer.

Example: You’re asked to automatically identify words indicating a location in science fiction books. You decide to use word2vec word embeddings. If your interviewer asks you “how were the word2vec embeddings created?”, she is digging into your understanding of word2vec and might be expecting you to question your choice. Seize this opportunity to display your mastery of the word2vec algorithm, and to ask a clarifying question. In fact, maybe the data distribution in the science fiction books is very different from the data distribution of the text corpora used to train word2vec. Maybe the interviewer is expecting you to say “although it will require significant amounts of data, we could train our own word embeddings on science fiction books.”

Show that you understand the development life cycle of an AI project.

Many candidates are only interested in what model they will use and how to train it. Remember that developing AI projects involves multiple tasks including data engineering, modeling, deployment, business analysis, and AI infrastructure.

Avoid clear-cut statements.

Because case studies are often open-ended and can have multiple valid solutions, avoid making categorical statements such as “the correct approach is …” You might offend the interviewer if the approach they are using is different from what you describe. It’s also better to show your flexibility with and understanding of the pros and cons of different approaches.

Study topics relevant to the company.

Deep learning case studies are often inspired by in-house projects. If the team is working on a domain-specific application, explore the literature.

Example 1: If the team is building an automatic speech recognition (ASR) software, review popular speech papers such as Deep Speech 2 (Amodei et al., 2015), audio datasets like Librispeech (Panayotov et al., 2015), as well as evaluation metrics like word error rate used to evaluate speech models.

Example 2: If the team is working on a face verification product, review the face recognition lessons of the Coursera Deep Learning Specialization ( Course 4 ), as well as the DeepFace (Taigman et al., 2014) and FaceNet (Schroff et al., 2015) papers prior to the onsite.

Example 3: If you’re interviewing with the perception team of a company building autonomous vehicles, you might want to read about topics such as object detection, path planning, safety, or edge deployment.

Articulate your thoughts in a compelling narrative.

Your interviewer will often judge the clarity of your thought process, your scientific rigor, and how comfortable you are using technical vocabulary.

Example 1: When explaining how a convolution layer works, your interviewer will notice if you say “ filter ” when you actually meant “ feature map ”.

Example 2: Mispronouncing a widely used technical word or acronym such as NER , MNIST, or CIFAR can affect your credibility. For instance, MNIST is pronounced “ɛm nist” rather than letter by letter.

Example 3: Show your ability to strategize by drawing the AI project development life cycle on the whiteboard.

Don’t mention methods you’re not able to explain.

Example: If you mention batch normalization , you can expect the interviewer to ask: “could you explain batch normalization?”.

Write clearly, draw charts, and introduce a notation if necessary.

The interviewer will judge the clarity of your thought process and your scientific rigor.

Example: Show your ability to strategize by drawing the AI project development life cycle on the whiteboard.

When you are not sure of your answer, be honest and say so.

Interviewers value honesty and penalize bluffing far more than lack of knowledge.

When out of ideas or stuck, think out loud rather than staying silent.

Talking through your thought process will help the interviewer correct you and point you in the right direction.

IV   Resources

You can build AI decision making skills by reading deep learning war stories and exposing yourself to projects . Here’s a list of useful resources to prepare for the deep learning case study interview.

In deeplearning.ai ’s course Structuring your Machine Learning Project , you’ll find insights drawn from Andrew Ng’s experience building and shipping many deep learning products. This course also has two “flight simulators” that let you practice decision-making as a machine learning project leader. It provides “industry experience” that you might otherwise get only after years of ML work experience.

• Deep Learning intuition is an interactive lecture illustrating AI decision making skills with examples from image classification, face recognition, neural style transfer, and trigger-word detection.
• In Full-cycle deep learning projects and Deep Learning Project Strategy , you’ll learn about the lifecycle of AI projects through concrete examples.
• In AI+Healthcare Case Studies , Pranav Rajupurkar presents challenges and opportunities for building and deploying AI for medical image interpretation .
• The popular real-time object detector YOLO (Redmon et al., 2015) was originally written in a framework called Darknet. Darkflow (Trieu) translates Darknet to Tensorflow and allows users to leverage transfer learning, retrain or fine-tune their YOLO models, an export model parameters in formats deployable on mobile.
• OpenPose (Cao et al., 2018) is a real-time multi-person system that can jointly detect human body, hand, facial, and foot keypoints on single images. You can find the authors’ code in the Git repository openpose .
• Learn about simple and efficient implementations of Named Entity Recognition models coded in Tensorflow in tf_ner (Genthial, 2018).
• By studying the code of ChatterBot (Cox, 2018), learn how to program a trainable conversational dialog engine in Python.
• Companies use convolutional neural networks (CNNs) for an assortment of purposes. They care about how accurately a CNN completes a task, and in many cases, about its speed. In Faster Neural Networks Straight from JPEG , Uber scientists (Gueguen et al.) describe an approach for making convolutional neural networks smaller, faster, and more accurate all at the same time by hacking libjpeg and leveraging the internal image representations already used by JPEG, the popular image format. Read carefully, and scrutinize the decisions making process throughout the project.
• Prediction models have to meet many requirements before they can be run in production at scale. In Using Deep Learning at Scale in Twitter’s Timelines , Twitter engineers Koumchatzky and Andryeyev explain how they incorporated deep learning into their modeling stack and how increased both audience and engagement on Twitter.
• Network quality is difficult to characterize and predict. While the average bandwidth and round trip time supported by a network are well-known indicators of network quality, other characteristics such as stability and predictability make a big difference when it comes to video streaming. Read Using Machine Learning to Improve Streaming Quality at Netflix (Ekanadham, 2018) to learn how machine learning enables a high-quality streaming experience for a global audience.

A convolution layer's filter is a set of trainable parameters that convolves across the convolution layer's input.

A feature map is one channel of a convolution layer's output. It results from convolving a filter on the input of a convolution layer.

In natural language processing, NER refers to Named Entity Recognition. It is the task of locating and classifying named entity (e.g., Yann Lecun, Trinidad and Tobago, and Dragon Ball Z) in text into pre-defined categories such as person names, organizations, locations, etc.

Batch normalization is a technique for improving the speed, performance, and stability of artificial neural networks. It was introduced by Ioffe et al. in 2015. (Wikipedia)

• Kian Katanforoosh - Founder at Workera, Lecturer at Stanford University - Department of Computer Science, Founding member at deeplearning.ai

Acknowledgment(s)

• The layout for this article was originally designed and implemented by Jingru Guo , Daniel Kunin , and Kian Katanforoosh for the deeplearning.ai AI Notes , and inspired by Distill .

Footnote(s)

• Job applicants are subject to anywhere from 3 to 8 interviews depending on the company, team, and role. You can learn more about the types of AI interviews in The Skills Boost . This includes the machine learning algorithms interview , the deep learning algorithms interview , the machine learning case study interview , the deep learning case study interview , the data science case study interview , and more coming soon.
• It takes time and effort to acquire acumen in a particular domain. You can develop your acumen by regularly reading research papers, articles, and tutorials. Twitter, Medium, and machine learning conferences (e.g., NeurIPS, ICML, CVPR, and the like) are good places to read the latest releases. You can also find a list of hundreds of Stanford students' projects on the Stanford CS230 website .

To reference this article, please use:

Workera, "Deep Learning Case Study Interview".

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Combination of multiple variables and machine learning for regional cropland water and carbon fluxes estimation: a case study in the haihe river basin.

1. Introduction

2. study area and data collection, 2.1. study area, 2.2. satellite data, 2.3. flux data, 3. methodology, 3.1. machine learning methods.

• Random Sampling—Using the bootstrap sampling method to randomly extract multiple sample sets from the original training data set, with each sample set having the same size as the original data set;
• Decision Tree Construction—For each sample set, construct a decision tree. During the tree construction process, introduce randomness to increase the diversity of the trees, such as randomly selecting features for splitting;
• Each decision tree independently predicts new data;
• Ensemble Prediction—Averaging the prediction results of all decision trees to obtain the final prediction result.
• Network Initialization—Randomly set the weights and biases between neurons in each layer;
• Forward Propagation of Input Signals—Propagate the input signals forward through the network and calculate the output of neurons in each layer;
• Error Calculation—Calculate the error based on the network’s output and the desired output;
• Error Backpropagation—Propagate the error signal backward and adjust the weights and biases of neurons in each layer according to the gradient descent method or other optimization algorithms;
• Iterative Training—Repeat steps b to d until the maximum number of iterations.

3.2. Input Variables

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Cheng, M.; Liu, K.; Liu, Z.; Xu, J.; Zhang, Z.; Sun, C. Combination of Multiple Variables and Machine Learning for Regional Cropland Water and Carbon Fluxes Estimation: A Case Study in the Haihe River Basin. Remote Sens. 2024 , 16 , 3280. https://doi.org/10.3390/rs16173280

Cheng M, Liu K, Liu Z, Xu J, Zhang Z, Sun C. Combination of Multiple Variables and Machine Learning for Regional Cropland Water and Carbon Fluxes Estimation: A Case Study in the Haihe River Basin. Remote Sensing . 2024; 16(17):3280. https://doi.org/10.3390/rs16173280

Cheng, Minghan, Kaihua Liu, Zhangxin Liu, Junzeng Xu, Zhengxian Zhang, and Chengming Sun. 2024. "Combination of Multiple Variables and Machine Learning for Regional Cropland Water and Carbon Fluxes Estimation: A Case Study in the Haihe River Basin" Remote Sensing 16, no. 17: 3280. https://doi.org/10.3390/rs16173280

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Machine Learning-Facilitated Policy Intensity Analysis: A Proposed Procedure and Its Application

• Original Research
• Published: 03 September 2024

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• Su Xie 1 , 2 ,
• Hang Xiong   ORCID: orcid.org/0000-0002-4949-2777 1 , 2 ,
• Linmei Shang 3 &
• Yong Bao 4  

Policy intensity is a crucial determinant of policy effectiveness. Analysis of policy intensity can serve as a basis for policy impact evaluation and enable policymakers to make necessary adjustments. Previous studies relied on manual scoring and mainly addressed specialized policies with limited numbers of texts. However, when dealing with text-rich policies, the method inevitably introduced bias and was time-consuming. In this paper, we propose a procedure facilitated by machine learning to analyze the intensity of not only specified but also comprehensive policies with large amounts of texts. Our machine learning-based approach assigns scores to the policy measure dimension, then cross-multiplies with two other dimensions, policy title and document type, to calculate intensity. The efficacy of our approach was demonstrated through a case study of China’s environmental policies for livestock and poultry husbandry, which showed improved performance over traditional methods in terms of efficiency and objectivity.

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Funding was provided by National Natural Science Foundation of China (Grant Number: 72173050).

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Xie, S., Xiong, H., Shang, L. et al. Machine Learning-Facilitated Policy Intensity Analysis: A Proposed Procedure and Its Application. Soc Indic Res (2024). https://doi.org/10.1007/s11205-024-03416-6

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