📄️ Table of contents
Free discovery course on Python for Artificial Intelligence in 6 lessons. Understand why Python won, what NumPy and pandas are for, how notebooks fit the workflow, what scikit-learn does, and how PyTorch and TensorFlow differ. No installation required to follow.
📄️ 1. Why Python won
Lesson 1 of the free Python for AI course: why a slow interpreted language became the standard for machine learning, how it delegates computation to compiled code, and why the ecosystem effect made the outcome irreversible.
📄️ 2. NumPy and pandas
Lesson 2 of the free Python for AI course: what NumPy arrays solve that Python lists cannot, what pandas is genuinely for, vectorisation and broadcasting explained plainly, and the size at which pandas stops being the right tool.
📄️ 3. Notebooks and workflow
Lesson 3 of the free Python for AI course: what a notebook is for, why hidden execution state causes results that cannot be reproduced, why notebooks are painful in version control, and when to move code into proper Python files.
📄️ 4. scikit-learn
Lesson 4 of the free Python for AI course: what scikit-learn covers, why fit and predict are the same for every model, what a pipeline prevents, and the data leakage mistake that inflates scores without warning.
📄️ 5. Frameworks and environments
Lesson 5 of the free Python for AI course: the real difference between PyTorch and TensorFlow, which to learn first in 2026, what Hugging Face changed, and why dependency isolation is what makes a result reproducible.
📄️ 6. Recap and FAQ
Lesson 6 of the free Python for AI course: a one-page synthesis, the order in which to learn the stack, a table of which tool to reach for when, and answers to the 12 questions beginners ask most.
📄️ 7. Quiz and attestation
Test your understanding of the Python AI ecosystem with a free 5-question quiz, corrected and explained: NumPy versus lists, pandas limits, notebook state, scikit-learn pipelines and data leakage.