Our list of some of the best Machine Learning books & series in recent years. Get inspired by one or more of the following books.
- Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (2017)
- The Hundred-Page Machine Learning Book (2019)
- Machine Learning: An Applied Mathematics Introduction (2019)
- Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition (2017)
- Machine Learning For Absolute Beginners: A Plain English Introduction (Machine Learning For Beginners) (2018)
- Introduction to Machine Learning with Python: A Guide for Data Scientists (2016)
- Machine Learning: The Absolute Complete Beginner’s Guide to Learn and Understand Machine Learning From Beginners, Intermediate, Advanced, To Expert Concepts (2019)
- Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition (2019)
- Mathematics for Machine Learning (2020)
- Machine Learning: A Quantitative Approach (2018)
- Machine Learning with Python: The Ultimate Beginners Guide to Learn Machine Learning with Python Step by Step (2019)
- Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning (2018)
- MACHINE LEARNING WITH PYTHON: An introduction to Data Science with useful concepts and examples, step by step, BOOKS IN 1, FOR ABSOLUTE BEGINNERS AND NOT) (2019)
- Machine Learning (2017)
- Machine Learning Pocket Reference: Working with Structured Data in Python (2019)
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (2017)
.Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data.
to avoid counterfeit, make sure that the book . Avoid third-party sellers. , Research Director at Google, co-author of , the most popular AI textbook in the world: “Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 pages.
A fully self-contained introduction to machine learning. All that the reader requires is an understanding of the basics of matrix algebra and calculus.
Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition (2017)
Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka’s bestselling book, Python Machine Learning.
Machine Learning For Absolute Beginners: A Plain English Introduction (Machine Learning For Beginners) (2018)
Ready to crank up a virtual server and smash through petabytes of data? Want to add ‘Machine Learning’ to your LinkedIn profile?Well, hold on thereBefore you embark on your epic journey into the world of machine learning, there is some theory and statistical principles to march through first. But rather than spend $30-$50 USD on a dense long textbook, you may want to read this book first.
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions.
Machine Learning: The Absolute Complete Beginner’s Guide to Learn and Understand Machine Learning From Beginners, Intermediate, Advanced, To Expert Concepts (2019)
★★Buy the Paperback Version of this Book and get the Kindle Book version for FREE ★★Machine Learning: The Complete Beginner’s Guide to learn and Understand Machine Learning, gives you insights into what machine learning entails and how it can impact the way you can weaponize data to gain incredible insights.
Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data. Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems.
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics.
: Machine learning is a newly-reinvigorated field. It promises to foster many technological advances that may improve the quality of our life significantly, from the use of latest, popular, high-gear gadgets such as smart phones, home devices, TVs, game consoles and even self-driving cars, and so on, to even more fun social and shopping experiences.
Machine Learning with Python: The Ultimate Beginners Guide to Learn Machine Learning with Python Step by Step (2019)
We live in a world of data deluge where gigabytes of data are generated daily. It is possible that this data might not be very useful for our daily applications. Major setbacks in the use of such data may be due to the presence of loopholes in data links previously generated or the data might be too vast for the limited human mind. Machine learning in this book presents some of the solutions to the problems above.
Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning (2018)
This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work.
MACHINE LEARNING WITH PYTHON: An introduction to Data Science with useful concepts and examples, step by step, BOOKS IN 1, FOR ABSOLUTE BEGINNERS AND NOT) (2019)
Machine Learning is a branch of AI that applied algorithms to learn from data and create predictions – this is important in predicting the world around us.Today, ML algorithms accomplish tasks that until recently only expert humans could perform and, as machines get ever more complex and perform more and more tasks to free up our time, so it is that new ideas are developed to help us continually improve their spee…
Machine Learning (2017)
Printed in Asia – Carries Same Contents as of US edition – Opt Expedited Shipping for 3 to 4 day delivery –
With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project.Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data.
Best Machine Learning Books That You Need
We highly recommend you to buy all paper or e-books in a legal way, for example, on Amazon. But sometimes it might be a need to dig deeper beyond the shiny book cover. Before making a purchase, you can visit resources like Library Genesis and download some machine learning books mentioned below at your own risk. Once again, we do not host any illegal or copyrighted files, but simply give our visitors a choice and hope they will make a wise decision.
Probabilistic Machine Learning for Civil Engineers
Author(s): James-A Goulet
ID: 2942770, Publisher: The MIT Press, Year: April 14, 2020, Size: 24 Mb, Format: pdf
Machine Learning for High-Risk Applications
Author(s): Patrick Hall
ID: 3100663, Publisher: O'Reilly Media, Inc., Year: 2023, Size: 6 Mb, Format: epub
Designing Machine Learning Systems
Author(s): Chip Huyen
ID: 3059821, Publisher: O'Reilly Media, Inc., Year: 2022, Size: 3 Mb, Format: epub
Please note that this booklist is not absolute. Some books are truly record-breakers according to Washington Post, others are drafted by unknown authors. On top of that, you can always find additional tutorials and courses on Coursera, Udemy or edX, for example. Are there any other relevant resources you could recommend? Drop a comment if you have any feedback on the list.