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Machine Learning (with Claude Code)
Published 9/2026
Created by John Poh
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English + subtitle | Duration: 77 Lectures ( 4h 2m ) | Size: 3.3 GB
From Statistical Foundations to Applied Intelligence
What you'll learn
Build and evaluate supervised ML models, logistic regression, SVM, random forests, KNN, on real datasets using Python and scikit-learn. Derive the statistical foundations of ML, bias, variance, MSE, maximum likelihood - that explain why supervised models work and when they fail. Apply the bias-variance tradeoff, cross-validation, and metrics beyond accuracy (AUC-ROC, F1) to judge and defend whether a model is trustworthy. Apply unsupervised techniques, K-means clustering, PCA, topic modelling, and graph analytics, to find hidden structure in unlabelled data. Implement deep Q-networks with experience replay and target networks, the two engineering fixes that make deep reinforcement learning stable. Train reinforcement learning agents from scratch: Q-learning on FrozenLake, a DQN in PyTorch on CartPole, and PPO via Stable-Baselines3. Select the right ML paradigm and algorithm for any problem and explain your model choices clearly to both technical and non-technical audiences. Use Claude Code as an AI pair programmer to build, debug, and interpret ML models alongside specialist advisor agents.Requirements
Basic Python is required. You should be comfortable with variables, functions, loops, and importing libraries. No advanced Python needed. Familiarity with pandas and numpy, enough to load a CSV and run basic array operations. If you're rusty, a one-hour refresher before Module 1 is sufficient. A working terminal. You need to be able to open a terminal, navigate folders with cd, and run a Python script. No sysadmin experience required. Node.js installed, needed to install Claude Code (npm install -g @anthropic-ai/claude-code). Free and takes under five minutes to set up. No prior machine learning experience required, every concept is introduced from first principles with analogies before any mathematics. No advanced mathematics required. A basic grasp of mean, variance, and what a function is will get you through. All statistical concepts are built up from scratch as they arise. macOS, Linux, or Windows (WSL2). The labs run in a terminal environment. Windows users should have WSL2 set up; instructions are provided in the course setup guide.Description
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 training can collapse if you remove the target network. You will leave knowing not just how to run the models, but why they behave the way they do.
By the end, you will be able to look at a new problem, identify which paradigm of machine learning it calls for, choose an appropriate algorithm, build and evaluate it in Python, and explain what it tells you to someone who has never heard of a confusion matrix.
Enrol now and start building machine learning from the ground up, with an AI pair programmer in your corner every step of the way.
Who this course is for
Aspiring data scientists and ML engineers who want to move beyond tutorials and build real, working models they can explain and defend, not just run. Analysts and technical professionals with Python experience who are ready to add machine learning to their toolkit and apply it to their own domain (healthcare, finance, marketing, operations). Students in quantitative fields, statistics, computer science, engineering, economics, who want hands-on ML experience to complement their academic training. Career changers entering AI and data science who have learned Python but have not yet applied it to machine learning and want a structured, end-to-end path through all three paradigms. Developers and engineers who are curious about how AI systems actually learn, from the statistical foundations through to the deep RL algorithms now used to fine-tune large language models. Anyone who wants to learn ML the way it is actually practised, writing code in a terminal, debugging real errors, and having an AI pair programmer and specialist advisors available throughout.Homepage
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