Is Probabilistic Machine Learning: An Introduction better than Hands-On Machine Learning?

Probabilistic Machine Learning: An Introduction ranks higher in our list of AI and machine learning books (#2 against #6, score 92.5 against 87.0). Hands-On Machine Learning is best for: building models with scikit-learn and TensorFlow. Probabilistic Machine Learning: An Introduction is best for: probabilistic modeling and Bayesian methods.

Which is cheaper, Hands-On Machine Learning or Probabilistic Machine Learning: An Introduction?

Hands-On Machine Learning: Print editions from O'Reilly; price varies by seller. Probabilistic Machine Learning: An Introduction: Free draft PDF; hardcopy sold by MIT Press.

Comparison · AI and machine learning books

Hands-On Machine Learning vs Probabilistic Machine Learning: An Introduction: which is better in 2026?

Hands-On Machine Learning87.0#6 in best AI and machine learning books

Probabilistic Machine Learning: An Introduction92.5#2 in best AI and machine learning books

Probabilistic Machine Learning: An Introduction ranks higher in our list of AI and machine learning books (#2 against #6, score 92.5 against 87.0).

How we rank Share

87.0
Editorial score
92.5
1
Formats
2
No
Free to use
Yes

Which should you choose?

Choose Hands-On Machine Learning if you want

Building models with scikit-learn and TensorFlow.

Practical code for classical models and neural networks, built on scikit-learn, Keras and TensorFlow. It is paid with no free text, and it goes less deep into theory than the books above it.

Choose Probabilistic Machine Learning: An Introduction if you want

Probabilistic modeling and Bayesian methods.

A broad, current treatment of machine learning as probabilistic modeling, with a free draft PDF. It is more mathematical than the practical texts here and newer than Bishop's 2006 book.

The main differences

Probabilistic Machine Learning: An Introduction can be used for free; Hands-On Machine Learning requires payment.

Side by side

	Hands-On Machine Learning	Probabilistic Machine Learning: An Introduction
Score	87.0	92.5
Rank in list	#6 of 13	#2 of 13
Price	PaidPrint editions from O'Reilly; price varies by seller	FreeFree draft PDF; hardcopy sold by MIT Press
Free to use	No	Yes
Formats	Print	PDF, Print
Author	Aurélien Géron	Kevin P. Murphy
Level	Intermediate	Advanced
Free to read online	No	Yes
Latest edition	2022, O'Reilly	2022, MIT Press; draft PDF updated
Math required	Light: basic Python and algebra	Heavy: probability and calculus
Best for	Building models with scikit-learn and TensorFlow	Probabilistic modeling and Bayesian methods
Strengths	Teaches methods through code Covers scikit-learn, Keras and TensorFlow Recent 2022 edition	Free draft PDF and reproducible figure code One probabilistic framework for many models Published in 2022, after the author's 2012 text
Limits	Less mathematical depth than the theory texts No free full text online	Long and mathematical for self-study Free draft carries a non-commercial, no-derivatives license
Links	Visit site	Visit site

Questions about Hands-On Machine Learning vs Probabilistic Machine Learning: An Introduction

Is Probabilistic Machine Learning: An Introduction better than Hands-On Machine Learning?: Probabilistic Machine Learning: An Introduction ranks higher in our list of AI and machine learning books (#2 against #6, score 92.5 against 87.0). Hands-On Machine Learning is best for: building models with scikit-learn and TensorFlow. Probabilistic Machine Learning: An Introduction is best for: probabilistic modeling and Bayesian methods.
Which is cheaper, Hands-On Machine Learning or Probabilistic Machine Learning: An Introduction?: Hands-On Machine Learning: Print editions from O'Reilly; price varies by seller. Probabilistic Machine Learning: An Introduction: Free draft PDF; hardcopy sold by MIT Press.