Machine Learning Interview Questions and Answers (2026)

82 machine learning interview questions with answers, grouped by topic and maintained as an open source project since 2018. Free, no signup.

• Machine Learning Fundamentals7 questions
• Ensemble Methods3 questions
• Data Preprocessing4 questions
• Optimization6 questions
• Deep Learning13 questions
• Computer Vision13 questions
• Dimensionality Reduction7 questions
• Model Evaluation7 questions
• Statistics for ML3 questions
• ML Coding6 questions
• NLP13 questions

Every question, by topic

Machine Learning Fundamentals

• What's the trade-off between bias and variance?
• Explain over- and under-fitting and how to combat them?
• What is regularization, why do we use it, and give some examples of common methods?
• Can you explain the differences between supervised, unsupervised, and reinforcement learning?
• What’s the difference between a generative and discriminative model?
• Instance-Based Versus Model-Based Learning.
• What is Turing test?

Ensemble Methods

• Why do ensembles typically have higher scores than individual models?
• What's the difference between boosting and bagging?
• Explain Linear Regression, Logistic Regression, Decision Tree and Random Forest?

Data Preprocessing

• What is data normalization and why do we need it?
• What is an imbalanced dataset? Can you list some ways to deal with it?
• What is data augmentation? Can you give some examples?
• When to use a Label Encoding vs. One Hot Encoding?

Optimization

• What is gradient descent?
• What is cost function?
• Define Learning Rate.
• What is Momentum (w.r.t NN optimization)?
• What is the difference between Batch Gradient Descent and Stochastic Gradient Descent?
• Epoch vs. Batch vs. Iteration.

Deep Learning

• How does a neural network work?
• Why is ReLU better and more often used than Sigmoid in Neural Networks?
• List different activation neurons or functions.
• What is vanishing gradient?
• What are dropouts?
• What is batch normalization and why does it work?
• What is the significance of Residual Networks?
• Define LSTM.
• List the key components of LSTM.
• List the variants of RNN.
• What is the basic difference between LSTM and Transformers?
• What is Autoencoder, name few applications.
• What are the components of GAN?

Computer Vision

• Describe how convolution works. What about if your inputs are grayscale vs RGB imagery? What determines the shape of the next layer?
• Why do we use convolutions for images rather than just FC layers?
• What makes CNNs translation invariant?
• Why do we have max-pooling in classification CNNs?
• Given stride S and kernel sizes for each layer of a (1-dimensional) CNN, create a function to compute the receptive field of a particular node in the network. This is just finding how many input nodes actually connect through to a neuron in a CNN.
• Why would you use many small convolutional kernels such as 3x3 rather than a few large ones?
• Why do segmentation CNNs typically have an encoder-decoder style / structure?
• Implement non maximal suppression as efficiently as you can.
• How would you remove outliers when trying to estimate a flat plane from noisy samples?
• How does CBIR work?
• How does image registration work? Sparse vs. dense optical flow and so on.
• Talk me through how you would create a 3D model of an object from imagery and depth sensor measurements taken at all angles around the object.
• What are RCNNs?

Dimensionality Reduction

• How do you combat the curse of dimensionality?
• Explain Principal Component Analysis (PCA)?
• What is the difference between LDA and PCA for dimensionality reduction?
• What is t-SNE?
• What is the difference between t-SNE and PCA for dimensionality reduction?
• What is UMAP?
• What is the difference between t-SNE and UMAP for dimensionality reduction?

Model Evaluation

• Why do we need a validation set and test set? What is the difference between them?
• What is stratified cross-validation and when should we use it?
• Given that we want to evaluate the performance of 'n' different machine learning models on the same data, why would the following splitting mechanism be incorrect?
• What is Precision?
• What is Recall?
• Define F1-score.
• Explain how a ROC curve works.

Statistics for ML

• What’s the difference between Type I and Type II error?
• How Random Number Generator Works, e.g. rand() function in python works?
• What is the difference between Bayesian vs frequentist statistics?

ML Coding

• Implement connected components on an image/matrix.
• Implement a sparse matrix class in C++.
• Create a function to compute an integral image, and create another function to get area sums from the integral image.
• Implement SQRT(const double & x) without using any special functions, just fundamental arithmetic.
• Reverse a bitstring.
• Reverse a linked list in place.

NLP

• What is the difference between stemming and lemmatization?
• What do you know about Latent Semantic Indexing (LSI)?
• What do you know about Dependency Parsing?
• Name different approaches for text summarization.
• What approach would you use for part of speech tagging?
• Explain what is a n-gram model.
• Explain how TF-IDF measures word importance.
• What is perplexity used for?
• What is Bag-of-Words model?
• Explain how the Markov assumption affects the bi-gram model?
• What are the most common word embedding methods? explain each briefly.
• What are the first few steps that you will take before applying an NLP algorithm to a given corpus?
• List a few types of linguistic ambiguities.