Python for ML
Python for ML on AI-ML Companion: Essential Python and libraries for machine learning. 19 interactive modules with live visualizations, quizzes, and hands-on Python coding.
Every module in Python for ML is free and open to everyone - no account required.
Modules in this track
- Getting Started with Python (free) - Install Python, run code, and write your first program
- Python Basics (free) - Variables, data types, and control flow
- Strings & Files (free) - Text manipulation and reading data from files
- Functions & Classes (free) - Reusable code and OOP basics
- Iterables & Generators (free) - iter, next and yield: walk a million rows while holding one in memory
- Errors & Debugging (free) - Reading tracebacks and handling exceptions with try/except
- Modern Python (free) - Type hints, dataclasses, and pathlib: the Python modern libraries expect
- Idiomatic Python (free) - sorted with key, Counter, defaultdict, any/all and the right container: the loops you do not have to write
- Async Python (free) - async, await and gather: overlap the waiting that dominates API and I/O work
- Configuration & Secrets (free) - Environment variables, .env files and a validated settings object: one codebase, every environment
- Logging (free) - Levels, loggers, handlers and formatters: knowing what happened, after it happened
- Packaging & Project Structure (free) - sys.path, packages, pyproject.toml and pip install -e .: from scattered files to an importable project
- NumPy Fundamentals (free) - Arrays and vectorized operations
- NumPy Advanced (free) - Broadcasting, indexing, and linear algebra
- Pandas Basics (free) - DataFrames and data manipulation
- Pandas Advanced (free) - Grouping, merging, and time series
- Data Visualization (free) - Matplotlib and Seaborn basics
- Scikit-learn Basics (free) - The ML workflow: fit, predict, evaluate
- ML Pipelines (free) - Preprocessing, pipelines, and cross-validation