Machine Learning and AI

Understand AI vocabulary, train simple models, evaluate them responsibly, and know where major libraries fit.

Open this lesson in Google Colab

How to use this chapter hands-on

This chapter is about Machine Learning and AI. Do not read it like a reference manual. Use it as a sequence of short labs. For each lesson, open the Colab notebook from the button above, run Example 1, write a prediction, and then change one small part of the code.

By the end of the chapter, you should have one small artifact: a notebook, a script, a trace table, or a project note. The artifact should show one working example, one controlled variation, and one error or surprising result that you investigated calmly.

If time is short, study one lesson deeply instead of skimming all of them. A deeply understood example is one you can run again, explain in plain language, modify safely, and debug when it breaks.

Why this chapter matters

Understand AI vocabulary, train simple models, evaluate them responsibly, and know where major libraries fit.

Lessons are now grouped by coherent learner topics. A page may contain multiple short quizzes when the page has multiple sections, so students can check one idea before moving to the next.

NoteGuiding questions

• Which lessons belong in this chapter?
• What should I review before moving on?
• How do the lesson sections build one practical skill?

Lesson path

• Concepts and Datasets — Distinguish AI concepts and identify features, labels, and datasets.
• Training and Evaluation — Split data, fit a first model, evaluate predictions, and recognize overfitting.
• Deep Learning Libraries — Understand tensors and where PyTorch and TensorFlow/Keras fit.
• Transformers Overview — Understand transformer models and why they matter for modern AI.
• Responsible AI — Use AI tools with attention to privacy, bias, evaluation, and accountability.

Chapter checkpoint

Use this short checkpoint before starting the chapter.

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