Python for AI

Every lesson: deep notes, curated lectures, an animated explainer, a hands-on lab, practice questions and a timed exam.

1. Variables and data typesLab: Python foundations repo lab. Assignment: Write typed variables for a small dataset.
2. Operators and expressionsLab: Python foundations repo lab. Assignment: Implement calculation functions.
3. if/elif/elseLab: Rule-based classifier lab. Assignment: Implement decision rules.
4. for/while loopsLab: Batch processor lab. Assignment: Process a list of records.
5. Lists, tuples, sets, dictionariesLab: Dataset manipulation lab lab. Assignment: Transform nested records.
6. Functions and scopeLab: Reusable feature library lab. Assignment: Build reusable preprocessing functions.
7. Modules and packagesLab: Mini Python package lab. Assignment: Split code into modules.
8. Exceptions and error handlingLab: Robust data loader lab. Assignment: Handle malformed input.
9. Classes and OOPLab: Dataset object model lab. Assignment: Create Dataset and Model classes.
10. Iterators and generatorsLab: Streaming data reader lab. Assignment: Process records without loading all data.
11. Virtual environmentsLab: Reproducible project lab. Assignment: Create isolated environment and requirements file.
12. NumPy arraysLab: Numerical computing notebook lab. Assignment: Implement vector/matrix operations.
13. Pandas dataframesLab: Data cleaning pipeline lab. Assignment: Clean, join and aggregate a dataset.
14. Matplotlib visualizationLab: EDA report lab. Assignment: Create five decision-useful charts.
15. Python mini-capstoneLab: Sales analytics CLI lab. Assignment: Build an end-to-end Python data-analysis CLI.

Tools introduced: pytest, NumPy, pandas, Matplotlib