Machine learning practice problems

Expectation, variance, Bayes' rule and entropy — computed from their definitions, so that the notation in later sections reads as arithmetic rather than decoration.

Master the fundamental data structure of numerical computing in Python. Learn to create, index, and manipulate n-dimensional arrays efficiently.

Raw data is messy. Learn normalization, standardization, and feature engineering, the critical steps that happen before any model sees data.

Build your intuition for deep learning from scratch. Understand layers, activations, and why depth enables hierarchical feature learning.

The simplest unsupervised algorithm: assign points to the nearest centroid and update centroids until convergence. Deceptively powerful in practice.

Agents learn by interacting with an environment. Rewards shape behavior through the Markov Decision Process framework, the basis of game-playing AI.

Training from scratch is expensive. Pre-trained models encode rich representations that transfer across tasks, the backbone of modern applied ML.

Real-world data arrives with outliers, duplicates, missing values, and evolving schemas. Mastering cleaning pipelines is the unglamorous work that makes or breaks model performance.