Best ML and DS textbooks

Description
Machine learning and Data Science and related Computer Science textbooks I've seen recommended a lot. Some Natural Language Processing applications sprinkled in since that's my field. No computer vision. Stats books have been moved to their own list.
1
Pattern Recognition and Machine Learning
by Christopher M. Bishop
499 members
3 reviews
4.1
2 Members
michelleqcy, ShiguangWU
Explanations
michelleqcy: One of the venerable books from before Deep Learning took off. Quite Bayesian.
2
Neural Networks for Pattern Recognition
by Christopher M. Bishop
169 members
4.1
2 Members
michelleqcy, ShiguangWU
Explanations
michelleqcy: Another Bishop book but from after Deep Learning took off.
3
Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
by Foster Provost
335 members
13 reviews
½ 3.6
Member
michelleqcy
Explanations
michelleqcy: A good non-technical introduction
4
An Introduction to Statistical Learning: with Applications in R
by Gareth James
331 members
2 reviews
½ 4.6
Member
michelleqcy
Explanations
michelleqcy: One of the best beginner books for understanding the theory behind Machine Learning.
5
Learning From Data
by Yaser S. Abu-Mostafa
105 members
½ 4.3
Member
michelleqcy
Explanations
michelleqcy: Abu-Mostafa's Caltech class is excellent. Another theoretical book.
6
Information Theory, Inference and Learning Algorithms
by David J. C. MacKay
361 members
1 review
½ 4.3
Member
michelleqcy
Explanations
michelleqcy: One of the venerable books from before Deep Learning etc took off.
7
Machine Learning: A Probabilistic Perspective
by Kevin P. Murphy
166 members
½ 4.4
Member
michelleqcy
Explanations
michelleqcy: Slightly more modern theory book. Quite Bayesian.
8
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
by Trevor Hastie
612 members
1 review
4.2
Member
michelleqcy
Explanations
michelleqcy: Like ISLR, this is by Hastie, but it's more technical. The advice is definitely to start with ISLR.
9
Python Machine Learning
by Sebastian Raschka
121 members
4.2
Member
michelleqcy
Explanations
michelleqcy: A good starter for actually beginning to code ML applications.
10
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
by Aurélien Géron
470 members
3 reviews
½ 4.4
Member
michelleqcy
Explanations
michelleqcy: Another good practical ML book.
11
Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD
by Jeremy Howard
51 members
Member
michelleqcy
Explanations
michelleqcy: Howard's FastAI course is supposedly pretty good. This is the book that goes with it.
12
Speech and language processing : an introduction to natural language processing, computational linguistics, and speech recognition
by Daniel Jurafsky
393 members
3 reviews
½ 4.3
Member
michelleqcy
Explanations
michelleqcy: A good pre-DL book about NLP. There's an updated edition available as PDF online.
13
Foundations of Statistical Natural Language Processing
by Christopher D. Manning
311 members
2 reviews
½ 4.3
Member
michelleqcy
Explanations
michelleqcy: Another originally pre-DL book on NLP.
14
Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning
by Delip Rao
31 members
4
Member
michelleqcy
Explanations
michelleqcy: Probably one of the better modern practical NLP books.
15
Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Methods
by Cameron Davidson-Pilon
39 members
4.2
Member
michelleqcy
Explanations
michelleqcy: Practical Bayesian learning methods