---
# System prepended metadata

title: ML-1

---

# ML-1
These are the resources covering the topics of Machine Learning following ML-0, one step higher from the basics. Some of these topics are quite mathematically intensive, therefore the resources have been well-equipped with intuitive as well as theoretical material, which are expected to be thoroughly studied.

Kindly contact Swadesh and Tushar for any queries regarding these resources.
## Compulsory Resources

- [Hands-On ML TextBook](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://www.youtube.com/playlist?list=PLakf6XoaFMqKLBIkOTGxUgABAeNPmbv9g)
- PCA blogs
    - [A friendly introduction to Principal Component Analysis](https://peterbloem.nl/blog/pca)
    - [Principal Component Analysis](https://gregorygundersen.com/blog/2022/09/17/pca/)
    - [Simplifying PCA: The Math, The Intuition, and Real-World Data Insights](https://ai.plainenglish.io/simplifying-pca-the-math-the-intuition-and-real-world-data-insights-d608e9b380ec)

- [PCA Video Lecture](https://www.youtube.com/watch?v=fkf4IBRSeEc&t=11s)
- SVD Video Lectures:
    - [Singular Value Decomposition (SVD): Overview](https://www.youtube.com/watch?v=gXbThCXjZFM&list=PLMrJAkhIeNNSVjnsviglFoY2nXildDCcv)
    - [Singular Value Decomposition (SVD): Mathematical Overview](https://www.youtube.com/watch?v=nbBvuuNVfco)
    - [Singular Value Decomposition (SVD): Matrix Approximation](https://www.youtube.com/watch?v=xy3QyyhiuY4)


- SVM Blogs
    - [Understanding the mathematics behind Support Vector Machines](https://shuzhanfan.github.io/2018/05/understanding-mathematics-behind-support-vector-machines/)

- StatQuest SVM PLaylist
    - [Support Vector Machines Part 1 (of 3): Main Ideas!!!](https://www.youtube.com/watch?v=efR1C6CvhmE)
    - [Support Vector Machines Part 2: The Polynomial Kernel (Part 2 of 3)](https://www.youtube.com/watch?v=Toet3EiSFcM)
    - [Support Vector Machines Part 3: The Radial (RBF) Kernel (Part 3 of 3)](https://www.youtube.com/watch?v=Qc5IyLW_hns)

- [MIT OpenCourseware SVM Video Lecture](https://www.youtube.com/watch?v=_PwhiWxHK8o)
## Supervised Learning:
- Ch 1-7 Hands on ML
- Skip videos 5-11 and 21 from StatQuest tree playlist and watch rest of them (make sure you go through these before watching lecture 10 of cs229)
- For svm and kernels refer chapter 5 from Hands-On ML and blogs
## Unsupervised Learning and Dimensionality Reduction:
- [K Means,DBSCAN, Hierarchical Clustering Playlist](https://hackmd.io/@tisanant/ry5AjOxX0#Supervised-Learning)
- Ch 8 and 9 of Hands-On ML
- Go through all the PCA and SVD blogs & lectures

## Exhaustive Resources 
- [Extensive resource for PCA and SVD](https://peterbloem.nl/files/unraveling-pca.pdf)
- [Mathematics for Machine Learning TextBook](https://mml-book.github.io/book/mml-book.pdf)
- [The Hundred-page Machine Learning Book
Book by Andriy Burkov](http://ema.cri-info.cm/wp-content/uploads/2019/07/2019BurkovTheHundred-pageMachineLearning.pdf) 

## Note 
- For Trees, SVD and PCA : first watch the videos from their respective playlists, and then move onto blogs, followed by the textbook for maximum depth
- MIT OpenCourseware SVM Lecture is to be referred only after the Statquest playlist as this an intermediate level lectured

## Practice Assignment (ML0+ML1):
- [https://hackmd.io/@HXdLo96_R_-Z9OVYMXDDAg/Hy4awaEWlx](https://hackmd.io/@HXdLo96_R_-Z9OVYMXDDAg/Hy4awaEWlx)
