Udemy – Machine Learning (with Claude Code) 2026-9

Udemy – Machine Learning (with Claude Code) 2026-9

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Description

Machine Learning (with Claude Code) is a companion course from Statistics to Applied Intelligence published by Udemy Online Academy. Rather than treating algorithms as black boxes, this course focuses on building real-world understanding: not just how each method works, but why it works, when it should be used, and how to critically evaluate its results in real-world situations. Course topics cover the three main branches of machine learning—supervised, unsupervised, and reinforcement learning—and provide participants with a comprehensive foundation in the field. In the supervised section, learners implement and evaluate models such as logistic regression, support vector machines, random forests, and K-nearest neighbors, and apply them to real-world data using Python and scikit-learn. In addition to coding, the course covers the statistical theory behind these techniques, including estimation theory, bias and variance, mean square error, and maximum likelihood, so that learners understand the reasoning behind the formulas rather than simply memorizing them. Model evaluation is considered a major topic.

Students learn to apply cross-validation and go beyond simple accuracy scores, using metrics such as AUC-ROC and F1 to determine whether a model is truly reliable or only superficially successful. The bias-variance trade-off is thoroughly explored as a tool for detecting overfitting and underfitting. In the unsupervised section, this course covers K-means clustering, principal component analysis (PCA), topic modeling, and graph analysis, preparing learners to discover hidden structure in unlabeled data—a critical skill for exploratory data analysis and feature engineering. Throughout the course, Claude Code acts as a programming partner, helping learners write, debug, and improve their machine learning implementations more quickly, while reinforcing best practices. By the end, participants will have both the theoretical foundation and practical experience necessary to confidently build, evaluate, and defend machine learning models in professional environments.

What you will learn in Machine Learning (with Claude Code):

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.
and …

Course specifications

Publisher: Udemy
Instructors: John Poh
Language: English
Level: Introductory to Advanced
Number of Lessons: 77
Duration: 4h 2m

Course topics

Machine Learning (with Claude Code)

Machine Learning (with Claude Code) Prerequisites

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.