Machine Learning Books Selection Which books should you look at for the study of machine learning

Machine learning. Machine Learning by Zhou Zhihua: This book by Zhou Zhihua is very suitable as an introduction to machine learning, and the examples in the book are very vivid and easy to understand.

PRML by Christopher Bishop: The PRML book is a bit Bayesian, and it may seem difficult for beginners, so you can read it in conjunction with the first two.

machine learning a probabilistic perspective learning by kevin p.Murphy: MLAPP is also a classic machine learning book that can be read in conjunction with PRML.

Natural language processing.

The Beauty of Mathematics by Wu Jun: This book by Mr. Wu Jun is suitable as a popular science book for introductory natural language processing.

foundations of statistical natural language processing by christopher d.Manning: This book was written by Manning, published in 1999, and is not covered in the recent popular Deep Learning for NLP, but you can refer to the course taught by his student Socher, CS 224N Ling 284.

Speech and Language Processing by Dan Jurafsky: Part of the third edition of the book has been updated with chapters that cover the techniques of Deep Learning for NLP.

1.Machine learning.

The first recommended book is Zhou Zhihua's "Machine Learning", known as the Watermelon Book, which is one of the classic introductory textbooks in the field of machine learning, and it is a large and complete book! Watermelons are used as an example in the content. If you really haven't been exposed to anything about machine learning before, then this book can probably be your first primer.

The book doesn't explain the theory in depth, but it is easy to understand each algorithm through examples.

The second book is to recommend Li Hang's statistical learning method, with a recommendation index of five stars, and a true fragrance index full of stars. This book explains the principles of machine learning and derives the formula in very, very detailed, and I believe that after reading this book, I will not say that machine learning is metaphysics. The second edition is now out.

The second edition is a bit thicker than the first. Using the formulas inside this book, it is highly recommended to get your hands dirty and push on the blank paper!

The third book is recommended to learn the concepts, principles, and formula derivation through the above two books, and then you can practice it. This book is a fitting primer for machine learning in action. The ** inside is knocked, and it has no effect if you don't knock it.

The biggest breakthrough in machine learning was deep learning in 2006. Deep learning is a class of machine learning that aims to mimic the human brain's thought processes and is often used for image and speech recognition.

1: Personally, I think Li Hang's "Statistical Learning Methods" is okay, and it is a basic machine learning introductory book. Lu ruichai.

3: Try to implement some of the most basic algorithms. The simplest example is the Bayesian classifier of Yu Pu, I implemented the first machine learning algorithm back then, and I am still very excited to think about it now. Later ones like SVM, decision trees can also be tried.

4: You must do a little application, otherwise, the feeling is all theory, and there will be no feeling at all. For example, the naïve Bayesian classifier above can be used to make a spam filtering system.

6: When you reach a certain level, you can gnaw on PRML, this is too classic, a bit like an introduction to algorithms in algorithms.

7: As for later, well, I'm still gnawing on prml... Let others say ...

Machine learning is a core subfield of artificial intelligence; It enables the computer to enter self-learning mode without explicit programming. When exposed to new data, these computer programs are able to learn, grow, change, and evolve on their own. My suggestion is to learn ML through **resources, not books.

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Related Resources:

Data Science, Deep Learning, and Machine Learning with Python.

May all be well!

The best book and tutorial on machine learning is The Introductory Guide to Semi-Automation in Electromechanics. This book contains a wide range of content on machines, which is simple and detailed, and it is worth recommending.

Deep Learning, a book that comprehensively explains the basic concepts of DL from the ground up to build a solid foundation in the field, explains linear algebra, probability and information theory, numerical computing, industry standards.

Of course, it is python high-performance programming published by the People's Posts and Telecommunications Publishing House, because this book will tell beginners how to learn in great detail.

《an introduction statistical to learning 》

the elements of statistical

They're all about statistical learning, or machine learning methods. The former can be seen as a simplified version of the latter. It's a little more popular and simpler, and the latter one is a little bit harder. These two books can be read over and over again, with a different takeaway each time.