**** Teacher Li Hang’s “Statistical Learning Methods” can be said to be the introduction of machine learning treasure, many machine learning training courses, Internet enterprises interview, written questions, many reference this book. To solve the problem of slow download speed of Github, I packaged my Github warehouse into image files and put them into Baidu Cloud for download, which can be saved in 5 seconds. (Huang Haiguang)
Resources is introduced
**** When I was learning machine learning, Li Hang’s “Statistical Learning Methods” gave me great help. I tried to search and download the book from Github and modify the code by myself. I implemented the book with Python code and downloaded it on Github (about 7095+ STAR) :
Github.com/fengdu78/li…
In July this year, I attended the GMIS Summit held by The Heart of Machine. I met Teacher Li Hang and added her wechat account. After a brief exchange, the project I did was recognized by teacher Li Hang.
Figure: Code directory (IPynb format)
Figure: Code screenshot (IPynb format)
The book shows
The first edition of Statistical Learning Methods was published in 2012. It describes statistical machine learning methods, mainly some commonly used supervised learning methods. The second edition adds some commonly used unsupervised learning methods, so this book covers the main content of traditional statistical machine learning methods.
Buy link (click on the mini program to buy) : \
Comparing the two editions, the first twelve chapters are the same, and the second edition contains more unsupervised learning:
Second Edition Course Catalogue:
Chapter 1 Supervision of palm practice
Chapter 1 Introduction to Statistical and supervised learning Chapter 2 Perceptron, Chapter 3 K-nearest Neighbor method, Chapter 4 Naive Bayes method, Chapter 5 Decision tree, Chapter 6 Logistic regression and preferred entropy model, Chapter 7 Support vector machine, Chapter 8 Promotion methods, Chapter 9 EM algorithm and its Generalization, Chapter 10 Hidden Markov model, Chapter 11 Conditional random fields Chapter 12 summary of supervised learning methods Chapter 2 unsupervised learning
Chapter 13 Introduction to unsupervised learning chapter 14 Clustering methods Chapter 15 Singular value decomposition Chapter 16 Principal component Analysis Chapter 17 Latent semantic analysis Chapter 18 Probabilistic latent semantic analysis Chapter 19 Markov chain Monte Carlo method \
Chapter 20 potential Dirichlet allocation
Chapter 21 PageRank algorithm \
Chapter 22 summary of unsupervised learning methods
Appendix A Gradient descent method
Appendix B Newton’s method and quasi-Newton’s method
Appendix C Lagrange duality
Appendix D Basic subspaces of matrices
Appendix E Definition of KL divergence and properties of dirichlet distribution
Baidu Cloud download file description
For some reason, domestic users access Github very slow, download resources often fail, so, for the convenience of readers, we put github content into image files to publish, for non-members can not save a large number of files at one time, I compressed all files into an ISO file, save can, only 5 seconds can save!
File description: ****
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lihang-code.iso
Github.com/fengdu78/li…
Full site mirror (code implementation of statistical Learning Methods)
Links:
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Pan.baidu.com/s/1mJmZlwdO…
Extraction code: BCGB
Note: my other Github has also been made into a mirror download, and will introduce each popular warehouse in the near future:
- Ng machine learning course resources
- Deep learning course resources
Machine learning beginner
Dr. Huang haiguang’s personal public account, Dr. Huang Haiguang’s personal Zhihu has 21000+ followers, and Github ranks among the top 120 in the world (29000+). This public number is committed to the direction of artificial intelligence science articles, for beginners to provide learning routes and basic information. Original works include: Personal Notes on Machine learning, notes on deep learning, etc.
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