In this era, there are 1,000 if not 10,000 books on artificial intelligence and big data, and the 28 books listed here are carefully selected. I can’t say that every one of them is worthy, but this list guarantees that there is no water book. Some suitable for entry, some suitable for advanced, not in accordance with the hierarchy, nonsense do not say, select a few books said the value of must-read, want to get partners can be in the public number (Pegasus) background reply number “29” view access!
Data collection
Common sense books
Professional books
In order not to be left behind, the best way is to continue to refresh your knowledge while maintaining hands-on experience. Success in this industry requires the perfect mix of project experience and skills. Although there are plenty of resources available online, we recommend some good physical books.
Data analysis -R language practice
Evaluation: data analysis book written specifically in R language, can be read after mastering the basics of R, focusing on the basic methods of data analysis, introduces some common analysis methods, comparison of the foundation.
Rating: four and a half stars
Use Python for data analysis
The book is written by the author of the Pandas module. It is summed up in one sentence: The Manual for pandas. Pandas is a necessary package for data analysis in Python.
Recommended rating: four stars
Python Learning Guide
Python is portable, powerful, and easy to use, making it ideal for writing standalone and scripted applications.
Data mining: R language combat
Evaluation: and the above “data analysis -R language combat” seems to be a series, basically common data mining methods are introduced, there are theoretical examples, suitable for entry.
Recommended rating: four stars
Data mining concepts and techniques
Evaluation: introductory book, more theory, seems to be a lot of graduate students to learn data mining teaching materials, very detailed, Meng Xiaofeng teacher’s translation or good, relatively many translation is very bad or can.
Recommended rating: four stars
Fundamentals of Statistical Learning: Data Mining, Reasoning, and Prediction
Evaluation: This book covers everything from guided learning (prediction) to unguided learning. Including topics such as neural networks, support vector machines, classification trees, and ascension, it is the most comprehensive book of its kind.
Statistical learning method
Evaluation: The mathematical derivation of the common algorithm of machine learning written by Dr. Li Hang is more detailed, and it is very good to understand the mathematical basis. If there is no mathematical basis, you can first look at the number of points of high generation convex optimization and other books. Suitable for learning with a certain foundation.
Rating: five stars
Introduction to data mining
Evaluation: intern colleague undergraduate course teaching materials, is also a big giant ah, foreigners write books, very easy to understand, very very detailed.
Recommended rating: four stars
Big Data
Evaluation :(this is a social science book, if you want to further study the principle of big data, you can choose other technical professional books). Taking the United States as an example, author Tu Zipei expressed the view that “data can not only govern the country, but also strengthen the country”, and put forward suggestions for China’s big data development strategy in the future.
MATLAB neural network 30 case analysis
For mature neural networks, this book gives MATLAB functions and call methods; For cutting-edge neural networks, efficient and concise programming algorithms are derived. For neural networks that need to be combined with other methods, this book also analyzes the principles of other methods, use methods and MATLAB functions, and even provides the corresponding toolbox for readers to call.
The rest of the other types of books after you get to check it out
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