Introduction
With the continuous heat in the AI field, more and more people are starting to self-learn AI. The first challenge of self-learning AI is how to find useful learning resources; there are too many resources online. Some users have shared machine learning resources in QQ groups through screenshots, with resources so abundant that it can make one give up self-learning AI—hundreds of gigabytes, which is simply terrifying.
To save everyone’s time, I searched for and downloaded resources related to machine learning from the internet, then categorized and filtered the resources that I found useful, hoping to assist you in your learning.
Suggestions: Machine learning is a field where the more you learn, the more confused you become. I do not recommend spending too much time reading various books; instead, choose one or two books for self-study to understand the principles and derivation processes of algorithms. For algorithm problems you encounter later, you can search on Baidu or Google. Once you have a foundation, you can quickly understand the principles of algorithms. Of course, practice is also essential. You can utilize the sklearn tool, which contains a large number of datasets and examples of machine learning. Through these examples, you can understand how to tune model parameters and also achieve the goal of self-learning Python. If you want to try your hand at a practical project, check out kaggle.
Table of Contents
1. Machine Learning
2. Deep Learning and Neural Networks
3. Python Self-Learning Books
4. Mathematics Related Materials
5. Algorithm Learning
Machine Learning
PRML is one of the most classic books on machine learning algorithms. This book analyzes machine learning algorithms from a Bayesian perspective and requires a certain level of mathematical foundation, such as matrix theory, probability theory, and stochastic processes. The first two chapters are quite classic and can be revisited for certain gains.
PRML English Version:
Link:
https://pan.baidu.com/s/1o-RcOKgZ_ysAEXG7wumtZA
Extraction Code: qppq
Machine Learning Based on Bayesian Theory (MLBOP)
Link:
https://pan.baidu.com/s/1XLZAtRD_kGQbbYkDUgIp9g
Extraction Code: ycw9
Classic machine learning textbooks for self-learning in China include Statistical Learning Methods, Machine Learning, and Machine Learning in Action.
Teacher Li Hang’s book Statistical Learning Methods analyzes machine learning algorithms from a statistical perspective. I find it very classic, with a lot of practical content and no fluff. The chapters on perceptrons and support vector machines left a deep impression on me.
Statistical Learning Methods:
Link:
https://pan.baidu.com/s/1OPLmzfPuVNn-btvHUb_xbw
Extraction Code: ff4c
Teacher Zhou Zhihua’s Watermelon Book is essential material for most self-learners.
Machine Learning:
Link:
https://pan.baidu.com/s/1fw6Qe04XFwkULGn73yCvSw
Extraction Code: bafu
Machine Learning in Action is a book that allows you to self-learn Python and machine learning. The content is not deeply explained but focuses on practice; the chapters on singular value decomposition and recommendation algorithms are particularly impressive.
Machine Learning in Action:
Link:
https://pan.baidu.com/s/1xf09bGgY3unWF-mUTH2bSQ
Extraction Code: zxwv
Personal Suggestion: Do not have too many books for self-learning machine learning; it’s enough to choose one or two to tackle. When you encounter difficult theories, search on Baidu or forums for solutions; having too many can easily lead to losing direction.
Teacher Andrew Ng’s public course videos are many people’s first love in machine learning algorithms, including mine.
Andrew Ng’s Machine Learning Public Course Videos (Ensure that you have the Thunder player and internet to play, with Chinese and English subtitles)
Link:
https://pan.baidu.com/s/1PbIdOKi-pY5WB_JolLuGyA
Extraction Code: p5nu
Machine Learning Exercises (with code):
Link:
https://pan.baidu.com/s/1vN6B_6wcD0WDt4-OVW7LHg
Extraction Code: 8g17
My notes from the machine learning public course:
Link:
https://pan.baidu.com/s/1437yGqOucPuR0g74LWt4Vw
Extraction Code: v8pi
Scikit-Learn Chinese Documentation:
Link:
https://www.cnblogs.com/wizardforcel/p/8016863.html
Deep Learning and Neural Networks
Neural Networks and Deep Learning
Link:
https://pan.baidu.com/s/1M0jkcoRFYHM-9H1NtaA-nw
Extraction Code: 5jyy
Neural Networks and Deep Learning (Chinese Version)
Link:
https://pan.baidu.com/s/14Q0kZESVyAQYjRWcXvawBA
Extraction Code: 5d8k
Python Deep Learning
Link:
https://pan.baidu.com/s/1F9cOsluBW37YmevMnWbLlA
Extraction Code: a6nf
Python Self-Learning Books
Concise Python Textbook
Link:
https://pan.baidu.com/s/1tdasje3VoCMJ3SYjUZFf8A
Extraction Code: f267
Python Programming: From Beginner to Practice
Link:
https://pan.baidu.com/s/1WgI_hgu39F88SeXxGZriLw
Extraction Code: rmta
Mathematics Related Materials
Tongji University Edition of Advanced Mathematics
Link:
https://pan.baidu.com/s/1NHTfALYU5T2f90UVldH0Zg
Extraction Code: 0lkc
<<Introduction to Linear Algebra>> Fifth Edition, I personally think it is the best introductory book on linear algebra.
Link:
https://pan.baidu.com/s/1lMug87xCSeq-1GS9g8-gQw
Extraction Code: 3tyn
Tongji University Edition of Linear Algebra
Link:
https://pan.baidu.com/s/1lfEo6ilxjE7iwRNYhtP9zA
Extraction Code: ueo0
Zhejiang University Edition of Probability Theory and Mathematical Statistics
Link:
https://pan.baidu.com/s/1KepVs9ANcnUITWSCVcB-Mg
Extraction Code: ek99
Zhang Xianda’s Matrix Analysis and Applications
Link:
https://pan.baidu.com/s/1XqrZ9WmGFn6ltdqiMTHyGw
Extraction Code: w2k6
Convex Optimization (Chinese Version)
Link:
https://pan.baidu.com/s/1F4btI2KYkXgr2aAodoTuww
Extraction Code: 6i5o
Algorithm Learning
Introduction to Algorithms (3rd Edition)
Link:
https://pan.baidu.com/s/1CJOTEvAvfDBpguuzEIZXww
Extraction Code: 3xsk
Algorithm Visualization
Link: https://pan.baidu.com/s/1CdHPcJQ0qc0R7HL_AxpTyw
Extraction Code: yapa
Let’s encourage each other with a final sentence!
Perhaps you are already very tired now, or maybe you see hope now, or even you have thoughts of giving up. However, please hold on a little longer, believe that good luck will always come, and this world will not disappoint a hardworking person.
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