Best Machine learning books
Machine learning is one of the hottest fields in the recent years. Therefore, isn’t a surprise when we hear about so many people who want to enter this world, wishing to start learning the basic elements of this subject.
If you are looking for Books about Machine learning to advance your Knowledge, here is the best list in various formats available for free:
- The Hundred-Page Machine Learning Book
- Real World Machine Learning [Free Chapters]
- An Introduction To Statistical Learning – Book + R Code
- Elements of Statistical Learning – Book
- Computer Age Statistical Inference (CASI) (Permalink as of October 2017) – Book
- Probabilistic Programming & Bayesian Methods for Hackers – Book + IPython Notebooks
- Think Bayes – Book + Python Code
- Information Theory, Inference, and Learning Algorithms
- Gaussian Processes for Machine Learning
- Data Intensive Text Processing w/ MapReduce
- Reinforcement Learning: – An Introduction (Permalink to Nov 2017 Draft)
- Mining Massive Datasets
- A First Encounter with Machine Learning
- Pattern Recognition and Machine Learning
- Machine Learning & Bayesian Reasoning
- Introduction to Machine Learning – Alex Smola and S.V.N. Vishwanathan
- A Probabilistic Theory of Pattern Recognition
- Introduction to Information Retrieval
- Forecasting: principles and practice
- Practical Artificial Intelligence Programming in Java
- Introduction to Machine Learning – Amnon Shashua
- Reinforcement Learning
- Machine Learning
- A Quest for AI
- Introduction to Applied Bayesian Statistics and Estimation for Social Scientists – Scott M. Lynch
- Bayesian Modeling, Inference and Prediction
- A Course in Machine Learning
- Machine Learning, Neural and Statistical Classification
- Bayesian Reasoning and Machine Learning Book+MatlabToolBox
- R Programming for Data Science
- Data Mining – Practical Machine Learning Tools and Techniques Book
- Machine Learning with TensorFlow Early access book
- Machine Learning Systems Early access book
- Hands‑On Machine Learning with Scikit‑Learn and TensorFlow – Aurélien Géron
- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data – Wickham and Grolemund. Great as introduction on how to use R.
- Advanced R – Hadley Wickham. More advanced usage of R for programming.
- Graph-Powered Machine Learning – Alessandro Negro. Combining graph theory and models to improve machine learning projects
- Machine Learning for Dummies
- Machine Learning for Mortals (Mere and Otherwise) – Early access book that provides basics of machine learning and using R programming language.
