Machine Learning (with Claude Code)

About this course

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.

How you'll learn:

The course runs inside your terminal using Claude Code as your AI pair programmer. Each lab is a live conversation; you write code, hit errors, and work through them with guidance rather than pre-recorded solutions. Three specialist advisor agents are available throughout: Elena for statistical rigour, Jamie for debugging and engineering judgment, and Marcus for translating results into decisions a business can act on. This mirrors how machine learning is actually practised on a team.

What makes this course different:

Most courses skip the foundations that make the algorithms make sense. This one starts with estimation theory, bias, variance, mean squared error, maximum likelihood, because those concepts are the reason a random forest generalises better than a single decision tree, and the reason your DQN t

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