Books for Data Mining

Entry books:

1. In-Depth Data Analysis (Douban)This book is quite simple. The basic content is involved, and it is quite clear. Finally, we talked about R as a plus. Difficulty level: very easy.
2. Beer and diapers (Douban)In this case, things are the most typical examples. Difficulty level: very easy.
3. Data beauty (Douban)Each chapter of an introductory book solves a specific problem and even contains code, which is very helpful for understanding the application fields and practices of data analysis. Difficulty: Easy.
4. Beauty of Mathematics (Douban)This book is amazing. It's a good start!

Data analysis:

1. Scipy and numpy (Douban)This book can be classified as a data analysis book, because numpy and scipy are really powerful.
2. Python for data analysis (Douban)The author is the author of the pandas package. He has read his speech at the scipy conference. The example is very strong!
3. Bad Data Handbook (Douban)The author has a very different perspective on interesting books.

Applicable Tutorials:

1. Collective Smart Programming (Douban)The first book to learn data analysis, data mining, and machine learning personnel should carefully read. The author introduces algorithms in machine learning and data mining through practical examples, which are easy to understand and can execute Python code. Difficulty level: medium.
2. Machine Learning in Action (Douban)People explain complex and difficult machine learning algorithms clearly, with sporadic mathematical formulas, but for the purpose of clarity. And Python code. Awesome! At present, the Chinese Emy of Sciences teacher Wang Bin (Weibo: Wang Bin _ ictio) has translated this book machine learning practice (Douban). The quality of this book is very high, and Mr. Wang's translation quality is also very high. Difficulty level: medium. One of my graduate students is required!
3. Building Machine Learning Systems with Python (Douban)Although it is in English, it is easy to write, easy to understand, followed by Python code, helping to understand.
4. Introduction to data mining (Douban)A good book in Data Mining textbooks in recent years has been used as a teaching material by data mining courses in many American universities. It is not recommended by Mr. Jiawei Han, I personally think that book is not easy for beginners to understand. Difficulty level: medium.
5. Machine Learning for hackers (Douban)We also use examples to explain machine learning algorithms. We can use R to learn R while learning R.

Slightly more professional:

1. Introduction to semi-Supervised Learning (Douban)Semi-supervised learning must read the required books.
2. Learning to rank for information retrieval (Douban)Liu Tianyan, a teacher from the Microsoft sub- Emy, wrote about LTr. I recommend it!
3. Learning to rank for information retrieval and natural language processing (Douban)Mr. Li Hang's book on LTr was also his book at the time of the Emy of Microsoft. It can be seen that the Emy of Microsoft has made great contributions to LTR research.
4. Recommendation System Practice (Douban)Needless to say, this book is the first book that the research and recommendation system must read.
5. Graphical models, exponential families, and variational inference (Douban)This is Mr. Jordan and his proud disciple Martin J Wainwright.The publication number on the foundation of machine learning research can be downloaded for free, which is hard to understand. However, once you read it, the related content of the graphical model can be flattened.
6. Natural Language Processing with Python (Douban)NLP is a classic. In fact, it mainly refers to the nltk package. However, the nltk package covers almost a lot of NLP content!

Machine learning materials:

1. The elements of Statistical Learning (Douban)This book has a corresponding Chinese version: statistical learning basics (Douban). The book is equipped with an R package, which is very nice! You can refer to the code learning algorithm.
2. Statistical Learning Method (Douban)We strongly recommend that you use the best practices of instructor Li Hang. Difficulty level: hard.
3. Machine Learning (Douban)Professor Kevin murrphy, a new book published last year, is a young and promising representative of the machine learning field. This book is a masterpiece of him. After writing it, I went to Google. There was no better combination of industry, university, and research.
4. Machine Learning (Douban)This is not the book above! This book is called: Machine Learning: An algorithmic perspective. I used my postgraduate textbooks before. It is easy to understand because of the Code.
5. Pattern Recognition and machine learning (Douban)Classic.
6. Bayesian reasoning and machine learning (Douban)I can see the name. The Bayesian school book contains a lot of content. A figure summarizes the relationship between the design algorithms in machine learning.
7. Probabilistic graphical models (Douban)Let me know if anyone has finished reading this book.
8. Convex Optimization (Douban)There is no better teaching material in convex optimization. Course is also very good, Stephen took the paper step by step to push, Figure 1.1 point painting, great.

Books for Data Mining