Machine Learning (with Claude Code)

Course Overview

Machine Learning (with Claude Code) is a comprehensive course covering supervised, unsupervised, and reinforcement learning from first principles. It emphasizes understanding the statistical foundations behind machine learning algorithms, practical implementation in Python, and the ability to explain and defend model choices. The course integrates Claude Code, an AI pair programmer, to provide interactive coding labs and real-time debugging support.

Key Takeaways

• Build and evaluate supervised machine learning models including logistic regression, SVM, random forests, and KNN using Python and scikit-learn.
• Understand statistical foundations such as bias, variance, mean squared error, and maximum likelihood to explain model behavior and limitations.
• Apply bias-variance tradeoff, cross-validation, and advanced metrics like AUC-ROC and F1 to assess model trustworthiness.
• Use unsupervised techniques like K-means clustering, PCA, topic modeling, and graph analytics to discover hidden structures in data.
• Implement deep reinforcement learning algorithms including deep Q-networks with experience replay and target networks, and train agents using Q-learning, DQN in PyTorch, and PPO via Stable-Baselines3.
• Select appropriate machine learning paradigms and algorithms for different problems and communicate results effectively to technical and non-technical audiences.
• Leverage Claude Code as an AI pair programmer for building, debugging, and interpreting machine learning models with support from specialist advisor agents.

Prerequisites

• Basic Python knowledge including variables, functions, loops, and importing libraries; no advanced Python required.
• Familiarity with pandas and numpy sufficient to load CSV files and perform basic array operations.
• Ability to use a terminal to navigate folders and run Python scripts; no sysadmin experience needed.
• Node.js installed to run Claude Code (installation instructions provided).
• No prior machine learning or advanced mathematics experience required; all concepts introduced from first principles.
• Compatible with macOS, Linux, or Windows (with WSL2 for Windows users).

Target Learners

• Aspiring data scientists and machine learning engineers seeking to build and explain real working models.
• Analysts and technical professionals with Python experience wanting to apply machine learning in their domains.
• Students in quantitative fields looking for hands-on machine learning experience.
• Career changers entering AI and data science who want a structured end-to-end learning path.
• Developers and engineers curious about the statistical and algorithmic foundations of AI systems.
• Anyone interested in learning machine learning through practical coding, debugging, and AI-assisted programming.