There are many books for Machine learning, but this article will guide the best book for machine learning beginners level to advanced level. These books will clear your Machine learning decision doubts.
Machine learning is an algorithm, which has the ability to make decisions and predictions. It is an application of Artificial Intelligence(AI), which has the ability to learn. Example of Machine learning is decision making, online support, stock prise predictor, spam message filter, and etc. It is used to discover a pattern in data, and predict the output based on the data.
Best book for machine learning Beginners to Advanced You must Read
All the mentioned book below is top-ranked and best customer review books for machine learning. Most of the books are machine learning using python.
1. The Hundred-Page Machine Learning Book
- Author: Andriy Burkov
- Publisher: Andriy Burkov
- Total Pages: 136 pages
If you are looking only for the best book for machine learning beginners, then this book is just amazing. I like this book because of its explanations, and easy to understand for beginners. It has knowledge from beginning to advanced level with theoretically and practically.
The author has discussed all the complex topics, diagrams, and examples. It is also recommended to Data Science & Software Engineering.
The author of this book is well experienced senior AI Engineer. This book has a badge of the bestseller book and very positive customer reviews on e-commerce seller platforms.
2. Machine Learning For Absolute Beginners
- Author: Oliver Theobald
- Publisher: Scatterplot Press
- Total pages: 157 pages
Another of this book Machine learning for absolute beginners is by Oliver Theobald. It is recommended for absolute beginners, who have no coding experience. It has introduced all the core algorithms with easy to understand.
This book has explained visual examples. and core algorithms. It has mentioned all the practical examples using the programming language
3. Programming Collective Intelligence
- Author: Toby segaran
- Publisher: O′Reilly, 1st edition
- Total pages: 360 pages
If you love to learn web 2.0 applications, search ranking, Support vector machines, product recommendation, then this is best machine learning book for you. This book explains all practical problems like optimizations, Clustering, Prediction, data analytics, Building Recommender systems.
This book is especially for advanced levels, who want to deal with cognitive systems. You would get real feel of implementation in Machine learning. All the real-world trending topics have covered in the simplest way to understand.
4. Machine Learning
- Author: Tom M. Mitchell
- Publisher: McGraw Hill Education, 1st edition
- Total pages: 432 pages
This book has been written in an easy way to understand with all core examples. each chapter has discussed an algorithm with examples along with pseudo code. It is definitely recommended for you if you want to start your journey in Machine learning.
you need to have all the basic concepts of discrete mathematics, so better to understand discrete mathematics first before diving into this book.
5. Learning From Data
- Author: Yaser S. Abu-Mostafa, Hsuan T. L. and Malik Magdon-Ismail
- Publisher: AMLBook
- Total pages: 213 pages
Learning from Data is another best book for machine learning beginners. It has also covered all the topics of machine learning with theoretical as well as practical. If you want to have a good understanding of algorithms in Machine learning, then this book for you.
You need some mathematical prerequisites to further explore this machine learning book. A website associated with the book, which provides additional materials online.
6. Machine Learning For Dummies
- Author: John paul Mueller, and Luca Massaron
- Publisher: For Dummies. First edition
- Total pages: 399 pages
This book has covered wide topics like R and python coding, statics, Big Data, linear models, and many more. You can easily understand the concepts. According to this book, without having a machine learning there are no possibilities of web search results, real-time ads on web pages, and email spam filtering.
Authors are a Data scientist, and well experienced in networking and home security to database management. Luca Massaron himself is a data scientist. He is specialized in big data, turning in smart data with data mining.
7. Machine Learning using Python
- Author: U Dinesh Kumar Manaranjan p.
- Publisher: Wiley
This book has collections of python libraries by providing all real-life case studies. It has explained machine learning with python, Descriptive Analytics, and Predictive Analytics. You will also get advanced machine learning concepts such as random forest, boosting, recommender systems, and text analytics are covered.
8 Pattern Recognition and Machine Learning
- Author: Christopher M. Bishop
- Publisher: Springer
- Total pages: 738 pages
This book has covered almost every concept of Machine learning in this book. You don’t need any special prerequisite knowledge of machine learning, but better to have calculus and basic linear algebra concepts. This is just a perfect book that comes with algorithms graphical models.
This book is especially recommended for advanced learners, Ph.D. students, and researchers scholars. You can find all the advanced topics of signal processing, computer vision, data mining, and bioinformatics. It includes more than 400 exercises
You will find all solutions on the official site of this book. The author of this book is Deputy Director of Microsoft Research, chair holder of the University of Edinburgh. His other popular textbook is Neural Networks for Pattern Recognition”.
9. Machine Learning for Email: Spam Filtering
- Author: Drew Conway
- Total pages: 146 pages
If you have an interest in Email filtering, then this is a great machine learning book for you. You will learn how to filter emails, sort, and redirect email based on statistical patterns. This book covers ranking email by importance, testing of the classifier, and email content with R function.
10. The Elements of Statistical Learning
- Author: Trevor H., Robert T., Jerome Friedman.
- Publisher: Springer; 2nd edition
- Total pages: 745 pages
This book deals with statics on machine learning in the field of finance, marketing, and computer framework.
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