Machine learning from entry to abandonment

I recommend NTU Lin Xuantian's cornerstones and techniques.

In fact, there is a video by a professor from caltech. Personally, I think it is better than Lin Xuantian. Lin Xuantian's boss, Egyptian, has a slightly heavy accent.

The teaching materials of the above 3 videos are learn from data , There is a full version download on CSDN (https://download.csdn.net/download/shiyih/9671865), including the original version and the electronic version supplemented later,

watch ng video, recommend Stanford's blackboard teaching

diagram machine learning, Li Hang's statistics Learning, ISLR

Zhou Zhihua's book is really not! Recommended! Recommend! Using self-study, it feels like a weapon spectrum, each knowledge point is only a little bit of water

, and the video on pattern recognition and machine learning of the National University of Science and Technology last year on station B, although only 3/4 of the video The content, but the essence is

here. Basic introduction to traditional machine learning, you can continue to deepen or in-depth learning

:
1. ESLII, PRML, MLAPP choose one or refer to each other
2. The two steps of matrix theory/convex optimization
are interchangeable or Cross

DL:
Video of Li Hongyi of National Taiwan University,
fast.ai of station B,
ng video of station B, Netease Cloud Classroom

, you can start reading flowers after this.