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Reading list

AI books worth your time

Ordered by when they help rather than by fame. Several are free from their authors. None of these links are affiliate links.

  1. 01

    Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow

    Aurélien Géron

    Beginner → Intermediate

    The single best starting point. Code-first, current, and it explains why each model behaves the way it does.

  2. 02

    An Introduction to Statistical Learning

    James, Witten, Hastie & Tibshirani

    Beginner

    The statistical intuition behind supervised learning, free from the authors, with Python labs in the second edition.

  3. 03

    Deep Learning

    Goodfellow, Bengio & Courville

    Advanced

    The reference text for the theory. Read it alongside practice rather than cover to cover.

  4. 04

    Designing Machine Learning Systems

    Chip Huyen

    Intermediate

    What happens after the notebook: data pipelines, deployment, monitoring and drift. Closest book to the job itself.

  5. 05

    Storytelling with Data

    Cole Nussbaumer Knaflic

    Beginner

    Analysis nobody understands is analysis nobody acts on. Short, and it will change your charts immediately.

  6. 06

    Speech and Language Processing

    Jurafsky & Martin

    Intermediate → Advanced

    The NLP canon, kept updated with transformer-era chapters and available free as drafts.

  7. 07

    The Hundred-Page Machine Learning Book

    Andriy Burkov

    Beginner

    A fast, honest map of the whole field. Useful for revision before interviews.

  8. 08

    Build a Large Language Model (From Scratch)

    Sebastian Raschka

    Intermediate

    Builds a working LLM step by step. The fastest way to stop treating transformers as magic.

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