https://mp.weixin.qq.com/s/JGXe2CmOdTweHjRJPOgiLg
1. Stanford "Probability and Statistics (Probability and Statistics)"
Link: https: //online.stanford.edu/courses/gse-yprobstat-probability-and-statistics
2.MIT "Linear Algebra (Linear Algebra)"
Link: https: //ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/
3. Stanford CS231N "convolution neural network for visual recognition (Convolutional Neural Networks for Visual Recognition)"
Link: https:? //Www.youtube.com/playlist list = PLzUTmXVwsnXod6WNdg57Yc3zFx_f-RYsq
4.fastai "real programmers deep learning (Practical Deep Learning for Coders)"
Link: https: //course.fast.ai/
5. Stanford CS224N "deep learning NLP (Natural Language Processing with Deep Learning) "
Link: https:? //Www.youtube.com/playlist list = PLU40WL8Ol94IJzQtileLTqGZuXtGlLMP_
6. Stanford on Coursera "machine learning" .
Link: https: //www.coursera.org/learn/machine-learning
7. Stanford "probabilistic graphical models special courses (Probabilistic Graphical Models Specialization)"
Link: https: //www.coursera.org/specializations/probabilistic-graphical-models
8. DeepMind "reinforcement learning introductory courses (Introduction to Reinforcement Learning)"
Link: https: //www.youtube.com/watch v = 2pWv7GOvuf0 & list = PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ?
9. Full Stack Deep Learning "full-stack depth study training camp (Full Stack Deep Learning Bootcamp)
Link: https: //fullstackdeeplearning.com/march2019
10. Coursera "How to Win data science competition: the top Kaggler learning (How to Win a Data Science Competition: Learn from Top Kagglers)"
Link: https: //www.coursera.org/learn/competitive-data-science