Udemy - Machine Learning (with Claude Code)
About Udemy - Machine Learning (with Claude Code)
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Machine Learning (with Claude Code) https://WebToolTip.com 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 statist
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Machine Learning (with Claude Code)
https://WebToolTip.com
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.