## Compulsory Resources
* [Hands on ML](http://14.139.161.31/OddSem-0822-1122/Hands-On_Machine_Learning_with_Scikit-Learn-Keras-and-TensorFlow-2nd-Edition-Aurelien-Geron.pdf)
* [CS-229 Playlist](https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU)
* [CS-229 Lecture Resources](https://github.com/maxim5/cs229-2018-autumn/tree/main)
* [StatQuest tree playlist](https://youtube.com/playlist?list=PLakf6XoaFMqKLBIkOTGxUgABAeNPmbv9g)
* [pca blogs](https://peterbloem.nl/blog/pca)
* [pca lecture](:https://youtu.be/I_c6w1SJSJs?si=G6VJVHU8IlNe08m-)
## Exhaustive Resources
* [Extensive resource for PCA and SVD](https://peterbloem.nl/files/unraveling-pca.pdf)
* [ISLP](https://hastie.su.domains/ISLP/ISLP_website.pdf)
## Supervised Learning:
* Lecture 5 to 7 and Lecture 10
* Ch 1-7 Hands on ML
* Skip videos 5,8,9,10,11,21 from statquest tree playlist and watch rest of them(watch this before watching lecture 10 of cs229)
* For svm and kernels go through the lecture notes(chapter 5,6)

## Unsupervised Learning and Dimensionality Reduction:
* Lecture lec 13 to 16
* Cover the pca lecture between lect 15 and 16
* Ch 8 and 9
* Go through all the pca blogs from 1 to 5