Udemy - Machine Learning (with Claude Code) torrent

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.