Most machine learning courses teach you how to run an algorithm. This one teaches you why it works, when to use it, and how to defend it to someone who is paying attention. Machine Learning (with Claude Code) covers all three paradigms of machine learning, namely, supervised, unsupervised, and reinforcement learning, in a single, coherent course built from first principles. You won’t be copying and pasting code you don’t understand. You will be writing it, breaking it, fixing it, and explaining it.
What you’ll build:
Across three modules and over twenty hands-on labs, you will fit logistic regression and random forest models , compress high-dimensional data with PCA, uncover hidden topics in document corpora with Latent Dirichlet Allocation, analyse influence in a real social network, train a Q-learning agent to navigate FrozenLake from scratch, implement a Deep Q-Network in PyTorch that learns to balance a pole on a cart, and apply Proximal Policy Optimization via Stable-Baselines3 to land a spacecraft. Every lab uses a real dataset or a real environment. None of them are toy examples dressed up to look impressive.
