# Hands-On Machine Learning vs Probabilistic Machine Learning: An Introduction

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| | Hands-On Machine Learning | Probabilistic Machine Learning: An Introduction |
|---|---|---|
| Score | 87.0 | 92.5 |
| Rank | #6 | #2 |
| Price | Paid | Free |
| Best for | Building models with scikit-learn and TensorFlow | Probabilistic modeling and Bayesian methods |
| 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 |

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. 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.