Python for ML

Modules in this track

• Getting Started with Python - Install Python, run code, and write your first program
• Python Basics - Variables, data types, and control flow
• Strings & Files - Text manipulation and reading data from files
• Functions & Classes - Reusable code and OOP basics
• Iterables & Generators - iter, next and yield: walk a million rows while holding one in memory
• Errors & Debugging - Reading tracebacks and handling exceptions with try/except
• Modern Python - Type hints, dataclasses, and pathlib: the Python modern libraries expect
• Idiomatic Python - sorted with key, Counter, defaultdict, any/all and the right container: the loops you do not have to write
• Async Python - async, await and gather: overlap the waiting that dominates API and I/O work
• Configuration & Secrets - Environment variables, .env files and a validated settings object: one codebase, every environment
• Logging - Levels, loggers, handlers and formatters: knowing what happened, after it happened
• Packaging & Project Structure - sys.path, packages, pyproject.toml and pip install -e .: from scattered files to an importable project
• NumPy Fundamentals - Arrays and vectorized operations
• NumPy Advanced - Broadcasting, indexing, and linear algebra
• Pandas Basics - DataFrames and data manipulation
• Pandas Advanced - Grouping, merging, and time series
• Data Visualization - Matplotlib and Seaborn basics
• Scikit-learn Basics - The ML workflow: fit, predict, evaluate
• ML Pipelines - Preprocessing, pipelines, and cross-validation