Summary of Machine Learning Learning Resources (Continuously Updated)¶

1 Overview¶

The purpose of this article is to summarize the machine learning reference materials that I usually use, facilitate my own review and update at any time, and also share with friends who need it. This article will be updated from time to time to ensure the relevance of the contents.

2 Online Courses¶

• Standford CS 229: Machine Learning - Bilibili
• Stanford Seminar CS25: Transformers United - YouTube
• 李沐学AI - Bilibili
• TensorFlow Developer Certificate Course - Zero to Mastery
• Intro to TensorFlow for Deep Learning - Udacity
• Machine Learning Crash Course - Google
• Machine Learning for Beginners - Microsoft
• Data Science for Beginners - Microsoft
• Kaggle courses

3 Technical Blogs & E-Books¶

3.1 Technical Blogs¶

• Kaggle Winner's Blog
• Visualizing machine learning one concept at a time - Jay Alammar
• Han Xiao tech blog
• Google AI
• Open AI
• The Unofficial Google Data Science Blog
• Andrej Karpathy blog
• Surge AI blog
• Paper with Code
• Diving into data
• Cloudera Blog
• Cookiecutter Data Science
• Hugging Face Chinese Blog

3.2 E-Books¶

• TensorFlow Developer Certificate Learning E-book
• 简单粗暴 TensorFlow 2 | A Concise Handbook of TensorFlow 2
• 30天吃掉那只TensorFlow2
• Machine Learning Interviews Book
• 动手学深度学习 - Amazon
• HuggingFace Course
• Forecasting: Principles and Practice - 3^rd Edition
• Rules of Machine Learning
• Machine Learning Glossary

4 GitHub Resources¶

• Data-Science-For-Beginners - Microsoft
• ML-For-Beginners - Microsoft
• Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow
• TensorFlow In Practice
• TensorFlow Examples
• Deep Learning Tuning Playbook

5 Notion Notes¶

• TensorFlow in Practice on Coursera Notes
• MIT Introduction to Deep Learning Notes

6 Competition Experience¶

• 2021 Kaggle All Event TOP Solution Summary

6.1 Exploratory Data Analysis¶

• Exploratory Data Analysis with Pandas
• Comprehensive data exploration with Python

6.2 Feature Engineering¶

• Feature Engineering Techniques
• IEEE - FE for Local test

6.3 Tabular Data Modeling¶

• Tabular Data Binary Classification: All Tips and Tricks from 5 Kaggle Competitions
• Data Science for tabular data: Advanced Techniques
• Tabular Classification - Tips and Tricks
• Feature Ranking RFE, Random Forest, linear models

6.4 Time Series Modeling¶

• Time Series Analysis in Python
• Deep Learning for Time Series Forecasting
• Electricity price forecasting with DNNs (+ EDA)

7 Summary¶

This article will be continuously updated, and PRs are welcome to add resources.

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