Data Science Interview Questions & Answers

What is supervised machine learning?
How cross-validation works? What is K-fold cross-validation?
What is classification? Which models would you use to solve a classification problem?
What is logistic regression? When do we need to use it?
What is logistic regression? When do we need to use it?
What is accuracy? Is it a good model?
What is AUC (AU ROC)? When to use it?
What is the PR (precision-recall) curve?
Why do we need one-hot encoding?
What is regularization? What are some of its techniques?
What’s the difference between L2 and L1 regularization?
What is regression and linear regression?
If a weight for one variable is higher than for another, is it more important?
When do we need to perform feature normalization for linear models? When it’s okay not to do it?
What is feature selection?
What are the decision trees?
What is random forest? Why do we need randomization in random forest?
What are the main parameters of the random forest model?
What is gradient boosting trees?
What are some hyper-parameter tuning strategies?
How does a usual fully-connected feed-forward neural network work? ‍
What is ReLU? How is it better than sigmoid or tanh?‍
What are the main assumptions of linear regression?
How we can initialize the weights of a neural network?
What is backpropagation? How does it work? Why do we need it?
What's learning rate?
What is Adam? What’s the main difference between Adam and SGD?
What's a convolutional layer?
What's pooling in Convolutional Neural Networks (CNN)?
Are Convolutional Neural Networks (CNN) resistant to image rotations?
What are augmentations?
What is transfer learning? How does it work?
How can we use machine learning for text classification?
What’s the normal distribution? Why do we care about it?
What is bag of words and how is it used in text classification?
What are word embeddings?
What is clustering?
How does K-means work?
How does DBScan work?
What's singular value decomposition? How is it typically used for machine learning?
What is precision and recall at k?
What is a recommender system?
What is collaborative filtering?
What is a time series?
How do we check if a variable follows the normal distribution?
What is gradient descent? How does it work?
Which metrics for evaluating regression models do you know? What are MSE and RMSE?
What is the bias-variance trade-off?
How to validate your models?

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