--- template: overrides/blogs.html tags: - machine learning --- # Machine Learning学习资料汇总（持续更新） !!! info 作者：[Vincent](https://github.com/Realvincentyuan)，发布于2021-06-06，阅读时间：约6分钟，微信公众号文章链接：[:fontawesome-solid-link:](https://mp.weixin.qq.com/s/Y1VF1Iw8kg-JmOPbIVM1mw) ## 1 概述 本文旨在汇总本人平时常用的机器学习参考资料，方便自己随时查看、更新，同时也分享给有需要的朋友。本文将不定期更新，确保内容的时效性。 ## 2 在线课程 - [Standford CS 229: Machine Learning - Bilibili](https://www.bilibili.com/video/BV1pp4y1t7Na?spm_id_from=333.337.search-card.all.click) - [Stanford Seminar CS25: Transformers United - YouTube](https://www.youtube.com/watch?v=P127jhj-8-Y&list=PLoROMvodv4rNiJRchCzutFw5ItR_Z27CM&ab_channel=StanfordOnline) - [跟李沐学AI - Bilibili](https://space.bilibili.com/1567748478/channel/series) - [TensorFlow Developer Certificate Course - Zero to Mastery](https://dbourke.link/ZTMTFcourse) - [Intro to TensorFlow for Deep Learning - Udacity](https://www.udacity.com/course/intro-to-tensorflow-for-deep-learning--ud187) - [Machine Learning Crash Course - Google](https://developers.google.com/machine-learning/crash-course) - [Machine Learning for Beginners - Microsoft](https://microsoft.github.io/ML-For-Beginners/#/) - [Data Science for Beginners - Microsoft](https://microsoft.github.io/Data-Science-For-Beginners/#/) - [Kaggle courses](https://www.kaggle.com/learn) ## 3 技术博客 & 电子书 ### 3.1 技术博客 - [Kaggle Winner's Blog](https://medium.com/kaggle-blog) - [Visualizing machine learning one concept at a time - Jay Alammar](http://jalammar.github.io/) - [Han Xiao tech blog](https://hanxiao.io/) - [Google AI](https://blog.google/technology/ai/) - [Open AI](https://openai.com/) - [The Unofficial Google Data Science Blog](https://www.unofficialgoogledatascience.com/) - [Andrej Karpathy blog](http://karpathy.github.io/) - [Surge AI blog](http://blog.echen.me/) - [Paper with Code](https://paperswithcode.com/) - [Diving into data](http://blog.datadive.net/) - [Cloudera Blog](https://blog.cloudera.com/) - [Cookiecutter Data Science](http://drivendata.github.io/cookiecutter-data-science/) - [Hugging Face Chinese Blog](https://huggingface.co/blog/zh) ### 3.2 电子书 - [TensorFlow Developer Certificate Learning E-book](https://dev.mrdbourke.com/tensorflow-deep-learning/) - [简单粗暴 TensorFlow 2 | A Concise Handbook of TensorFlow 2](https://tf.wiki/) - [30天吃掉那只TensorFlow2](https://lyhue1991.github.io/eat_tensorflow2_in_30_days/chinese/) - [Machine Learning Interviews Book](https://huyenchip.com/ml-interviews-book/) - [动手学深度学习 - Amazon](https://zh-v2.d2l.ai/) - [HuggingFace Course](https://huggingface.co/course/chapter1/1) - [Forecasting: Principles and Practice - 3rd Edition](https://otexts.com/fpp3/index.html) - [Rules of Machine Learning](https://developers.google.com/machine-learning/guides/rules-of-ml#terminology) - [Machine Learning Glossary](https://ml-cheatsheet.readthedocs.io/en/latest/index.html) ## 4 GitHub 资源 - [Data-Science-For-Beginners - Microsoft](https://github.com/microsoft/Data-Science-For-Beginners) - [ML-For-Beginners - Microsoft](https://github.com/microsoft/ML-For-Beginners) - [Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow](https://github.com/ageron/handson-ml2) - [TensorFlow In Practice](https://github.com/lmoroney/dlaicourse) - [TensorFlow Examples](https://github.com/tensorflow/examples) - [Deep Learning Tuning Playbook](https://github.com/google-research/tuning_playbook) ## 5 Notion 笔记 - [TensorFlow in Practice on Coursera Notes](https://www.notion.so/Course-TensorFlow-in-Practice-on-Coursera-Notes-5f4f8915fe3342e2a69f75ef1986ba3b) - [MIT Introduction to Deep Learning Notes](https://www.notion.so/Course-MIT-Introduction-to-Deep-Learning-Notes-0e48ecc9ed7342b7b448956bed9e0e75) ## 6 竞赛经验 - [2021年Kaggle所有赛事TOP方案汇总](https://zhuanlan.zhihu.com/p/472915303) ### 6.1 探索性数据分析 - [Exploratory Data Analysis with Pandas](https://www.kaggle.com/code/kashnitsky/topic-1-exploratory-data-analysis-with-pandas/notebook) - [Comprehensive data exploration with Python](https://www.kaggle.com/code/pmarcelino/comprehensive-data-exploration-with-python) ### 6.2 特征工程 - [Feature Engineering Techniques](https://www.kaggle.com/competitions/ieee-fraud-detection/discussion/108575) - [IEEE - FE for Local test](https://www.kaggle.com/code/kyakovlev/ieee-fe-for-local-test/notebook) ### 6.3 表格型数据建模 - [Tabular Data Binary Classification: All Tips and Tricks from 5 Kaggle Competitions](https://neptune.ai/blog/tabular-data-binary-classification-tips-and-tricks-from-5-kaggle-competitions) - [Data Science for tabular data: Advanced Techniques](https://www.kaggle.com/code/vbmokin/data-science-for-tabular-data-advanced-techniques/notebook) - [Tabular Classification - Tips and Tricks](https://www.kaggle.com/competitions/amex-default-prediction/discussion/335892) - [Feature Ranking RFE, Random Forest, linear models](https://www.kaggle.com/code/arthurtok/feature-ranking-rfe-random-forest-linear-models) ### 6.4 时间序列建模 - [Time Series Analysis in Python](https://www.kaggle.com/code/kashnitsky/topic-9-part-1-time-series-analysis-in-python) - [Deep Learning for Time Series Forecasting](https://www.kaggle.com/code/dimitreoliveira/deep-learning-for-time-series-forecasting/notebook) - [Electricity price forecasting with DNNs (+ EDA)](https://www.kaggle.com/code/dimitriosroussis/electricity-price-forecasting-with-dnns-eda) ## 7 总结 本文将持续更新，也欢迎提交Pull Requests补充资源。