🤖 Machine Learning Interview Questions

Master ML interviews with questions on algorithms, neural networks, model training, and deployment

Explain the difference between supervised, unsupervised, and reinforcement learning

Easy

Supervised Learning: Learning from labeled data (input-output pairs). The model learns to map inputs to correct outputs.

Examples: Classification (spam detection), Regression (price prediction)

Unsupervised Learning: Learning patterns from unlabeled data. The model discovers hidden structure.

Examples: Clustering (customer segmentation), Dimensionality reduction (PCA)

Reinforcement Learning: Learning through trial and error with rewards/penalties. Agent learns optimal actions in an environment.

Examples: Game playing (AlphaGo), Robotics, Self-driving cars

Python

Implement a neural network from scratch and explain backpropagation

Hard

Neural networks learn by adjusting weights through backpropagation - computing gradients of loss with respect to weights using the chain rule, then updating weights via gradient descent.

Python

Explain overfitting, underfitting, and regularization techniques

Medium

Overfitting: Model learns training data too well, including noise. Poor generalization to new data.

Underfitting: Model is too simple to capture underlying patterns. Poor performance on both training and test data.

Regularization: Techniques to prevent overfitting by adding constraints.

Python

Implement a convolutional neural network (CNN) for image classification

Hard

CNNs use convolutional layers to automatically learn hierarchical features from images. They're highly effective for computer vision tasks.

Python

Explain model deployment and MLOps best practices

Medium

MLOps brings DevOps practices to machine learning, focusing on automation, monitoring, and reproducibility of ML systems in production.

Python

Interview Tips for Machine Learning