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Best Books for Machine Learning & Artificial Intelligence || AI & ML

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As a software engineer, machine learning/AI/Data Science are among the direction or career, IT professionals are very concerned about. With the aid of various projects included, you will discover that it’s intriguing to get the mechanics of several important machine learning algorithms they’re no more obscure since they thought. We have prepared the list of best / must-read 10 books for machine learning and artificial intellengence. These books are highly advised for students in academia, which provides the necessary mathematical background to totally appreciate deep learning in its present-day state.

Many students are getting more attracted towards various courses on Artificial Intelligence and Machine Learnig. Courses have its own importance, but it can not be compared with a book. A books has its own flow, by which one understand the evolution of subject topic and further advancement. It carries a story in it, through which one can understand the subject matter more effectively.

If you would like to learn in about all aspects of machine learning, buying many books may be the ideal approach. Some books explore and utilize only a type of ML, and that means you will need to find out more about. There are several affordable and well-written books about the topic.

These books will give a good foundation in the area of statistics. Also few of books explores neural networks and the way they influence our everyday lives and their application in machine learning.

Best books on machine learning

Best 10 Books for Machine Learning & Artificial Intelligence ( AI & ML)

  1. Machine Learning For Absolute Beginners: A Plain English Introduction (Machine Learning For Beginners)
  2. Machine Learning with R: Expert techniques for predictive modeling to solve all your data analysis problems
  3. Artificial Intelligence: A Modern Approach by Stuart Russell & Peter Norvig(3rd Edition)
  4. Machine Learning by Tom M. Mitchell
  5. An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) by Gareth James , Daniela Witten , et al.
  6. The Hundred-Page Machine Learning Book 
  7. by Andriy Burkov  (Author)
  8. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham and Garrett Grolemund 
  9. Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
  10. Deep Learning (Adaptive Computation and Machine Learning series)

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