# ML 0
Resources covering the basics of Machine Learning.
CS229 lecture videos are very important and we hope that you watch them properly beforehand. The theory of GLMs and the math can be a bit tough, but try your best to go through the resources before the lecture.

You can contact Aakash and Pranav for any doubts regarding these resources.

## Compulsory Resources
* [CS-229 Playlist](https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU)
* [CS-229 Lecture Notes](https://cs229.stanford.edu/notes2022fall/main_notes.pdf)
* [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)

## Exhaustive Resources (Optional)
* [Goodfellow chapter 5](https://www.deeplearningbook.org/contents/ml.html)
* [ISLP](https://hastie.su.domains/ISLP/ISLP_website.pdf.download.html)
## Supervised Learning:
Linear Regression, Logistic Regression, Generalized Linear Models (GLMs)
* Lecture 2 - 4
* Ch 1-4 Hands on ML

## K Means Clustering:
* Ch 9 Hands on ML
* Ch 10 (first part in unsupervised learning) CS229 notes

## Miscellaneous Topics (Cross validation, Bias Variance Tradeoff, etc.):
* Lecture 8, 9 (till bias-variance tradeoff)
* [Model Performance Evaluation](https://towardsdatascience.com/various-ways-to-evaluate-a-machine-learning-models-performance-230449055f15)
* [Key ML Metrics](https://neptune.ai/blog/performance-metrics-in-machine-learning-complete-guide)
* [Metrics in Imbalanced Datasets](https://archive.is/20210127024052/https://towardsdatascience.com/metrics-for-imbalanced-classification-41c71549bbb5)
* [Handling Imbalanced Datasets](http://archive.today/2023.05.10-084555/https://medium.datadriveninvestor.com/from-theory-to-practice-a-complete-guide-to-handle-imbalanced-dataset-7c6a625a3048)