* [Computer Age Statistical Inference (CASI)](https://web.stanford.edu/~hastie/CASI_files/PDF/casi.pdf) ([Permalink as of October 2017](https://perma.cc/J8JG-ZVFW)) - Book
* [Reinforcement Learning: - An Introduction](http://incompleteideas.net/book/the-book-2nd.html) ([Permalink to Nov 2017 Draft](https://perma.cc/83ER-64M3))
* [A Quest for AI](https://ai.stanford.edu/~nilsson/QAI/qai.pdf)
* [Introduction to Applied Bayesian Statistics and Estimation for Social Scientists](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.177.857&rep=rep1&type=pdf) - Scott M. Lynch
* [R for Data Science: Import, Tidy, Transform, Visualize, and Model Data](https://r4ds.had.co.nz/) - Wickham and Grolemund. Great introduction on how to use R language.
* [Machine Learning for Mortals (Mere and Otherwise)](https://www.manning.com/books/machine-learning-for-mortals-mere-and-otherwise) - Early access book that provides basics of machine learning and using R programming language.
* [Grokking Machine Learning](https://www.manning.com/books/grokking-machine-learning) - Early access book that introduces the most valuable machine learning techniques.
- [How Machine Learning Works](https://www.manning.com/books/how-machine-learning-works) - Mostafa Samir. Early access book that intorduces machine learning from both practical and theoretical aspects in a non-threating way.
- [Fighting Churn With Data](https://www.manning.com/books/fighting-churn-with-data) [Free Chapter] Carl Gold - Hands on course in applied data science in Python and SQL, taught through the use case of customer churn.
- [MLOps Engineering at Scale](https://www.manning.com/books/mlops-engineering-at-scale) - Carl Osipov - Guide to bringing your experimental machine learning code to production using serverless capabilities from major cloud providers.
- [AI-Powered Search](https://www.manning.com/books/ai-powered-search) - Trey Grainger, Doug Turnbull, Max Irwin - Early access book that teaches you how to build search engines that automatically understand the intention of a query in order to deliver significantly better results.
- [Ensemble Methods for Machine Learning](https://www.manning.com/books/ensemble-methods-for-machine-learning) - Gautam Kunapuli - Early access book that teaches you to implement the most important ensemble machine learning methods from scratch.
- [Machine Learning Engineering in Action](https://www.manning.com/books/machine-learning-engineering-in-action) - Ben Wilson - Field-tested tips, tricks, and design patterns for building Machine Learning projects that are deployable, maintainable, and secure from concept to production.
- [Privacy-Preserving Machine Learning](https://www.manning.com/books/privacy-preserving-machine-learning) - J. Morris Chang, Di Zhuang, G. Dumindu Samaraweera - Keep sensitive user data safe and secure, without sacrificing the accuracy of your machine learning models.
- [Automated Machine Learning in Action](https://www.manning.com/books/automated-machine-learning-in-action) - Qingquan Song, Haifeng Jin, and Xia Hu - Optimize every stage of your machine learning pipelines with powerful automation components and cutting-edge tools like AutoKeras and Keras Tuner.
- [Distributed Machine Learning Patterns](https://www.manning.com/books/distributed-machine-learning-patterns) - Yuan Tang - Practical patterns for scaling machine learning from your laptop to a distributed cluster.
- [Human-in-the-Loop Machine Learning: Active learning and annotation for human-centered AI](https://www.manning.com/books/human-in-the-loop-machine-learning) - Robert (Munro) Monarch - a practical guide to optimizing the entire machine learning process, including techniques for annotation, active learning, transfer learning, and using machine learning to optimize every step of the process.
- [Feature Engineering Bookcamp](https://www.manning.com/books/feature-engineering-bookcamp) - Maurucio Aniche - This book’s practical case-studies reveal feature engineering techniques that upgrade your data wrangling—and your ML results.
- [Metalearning: Applications to Automated Machine Learning and Data Mining](https://link.springer.com/content/pdf/10.1007/978-3-030-67024-5.pdf) - Pavel Brazdil, Jan N. van Rijn, Carlos Soares, Joaquin Vanschoren
- [Causal Machine Learning](https://www.manning.com/books/causal-machine-learning) - Robert Ness - Practical introduction to building AI models that can reason about causality.
- [Bayesian Optimization in Action](https://www.manning.com/books/bayesian-optimization-in-action) - Quan Nguyen - Book about building Bayesian optimization systems from the ground up.
- [Machine Learning Algorithms in Depth](https://www.manning.com/books/machine-learning-algorithms-in-depth)) - Vadim Smolyakov - Book about practical implementations of dozens of ML algorithms.
- [Optimization Algorithms](https://www.manning.com/books/optimization-algorithms) - Alaa Khamis - Book about how to solve design, planning, and control problems using modern machine learning and AI techniques.
* [Natural Language Processing in Action, Second Edition](https://www.manning.com/books/natural-language-processing-in-action-second-edition) Early access book
* [Deep Learning for Natural Lanuage Processing](https://www.manning.com/books/deep-learning-for-natural-language-processing) Early access book
* [Natural Language Processing in Action, Second Edition](https://www.manning.com/books/natural-language-processing-in-action-second-edition) Early access book
* [Getting Started with Natural Language Processing in Action](https://www.manning.com/books/getting-started-with-natural-language-processing) Early access book
