Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow
by Aurélien Géron (O'Reilly)
The book most practitioners recommend for going from ML basics to production-ready models.
Overview
Widely considered the best single ML book for practitioners, 'Hands-On ML' covers the full workflow: data preparation, classical ML with scikit-learn, and deep learning with Keras/TensorFlow, including CNNs, RNNs, transformers, and deployment. It balances just-enough theory with abundant, runnable code (the notebooks are free on GitHub even though the book is paid). A reference you'll return to for years.
At a Glance
- Topic
- ML
- Level
- Intermediate
- Format
- Book
- Cost
- Paid
- Duration
- Full book, self-paced
- Provider
- Aurélien Géron (O'Reilly)
- Hands-on
- Yes — code/exercises
- Certificate
- None
What You’ll Learn
- ✓End-to-end ML projects with scikit-learn
- ✓Deep learning with Keras and TensorFlow
- ✓CNNs, RNNs, transformers, and autoencoders
- ✓Training tricks and deployment
Highlights
- •The most-recommended practical ML book
- •Free companion notebooks on GitHub
Who It’s For
Best For
- ✓Practitioners who want one authoritative reference
Prerequisites
- •Python
- •Basic math
FAQ
What is Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow?
Aurélien Géron's best-selling practical guide to end-to-end machine learning and deep learning, with runnable notebooks on GitHub.
Is Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow free?
Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow is a paid resource.
What level is Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow for?
Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow is aimed at a intermediate audience. Recommended background: Python, Basic math.
How long does Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow take?
Expect roughly Full book, self-paced. Most learners work through it at their own pace.
What will I learn from Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow?
You'll learn: End-to-end ML projects with scikit-learn; Deep learning with Keras and TensorFlow; CNNs, RNNs, transformers, and autoencoders; Training tricks and deployment.