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Add Machine Learning resource recommendations #447

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docs: add ML resources
RudrakshParsai Oct 30, 2025
4a8c536
add book and lecture notes
RudrakshParsai Oct 30, 2025
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Binary file added ...On_Machine_Learning_with_Scikit-Learn-Keras-and-TensorFlow-2nd-Edition-Aurelien-Geron.pdf
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Expand Up @@ -286,3 +286,34 @@ https://www.udacity.com/course/reinforcement-learning--ud600
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## Rudraksh Parsai
<a href="https://www.linkedin.com/in/rudrakshparsai/">
<img align="left" width="82px" src="https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white" />
</a>

[![Generic badge](https://img.shields.io/badge/Batch-2024-<COLOR>.svg)](https://shields.io/)
```

1) Machine Learning (Andrew Ng — Coursera)
https://www.coursera.org/learn/machine-learning
This is where I started. Very beginner-friendly.
It builds intuition first instead of dumping math. Helped me actually understand what models are doing.

2) Deep Learning Specialization (Andrew Ng — Coursera)
https://www.coursera.org/specializations/deep-learning
This is where neural networks finally *made sense* for me.
Covers CNNs, RNNs, training tricks, how to not overfit etc.
Good step after the basics.

3) CS229 (Stanford)
http://cs229.stanford.edu/
I came to this later when I wanted the theory properly.
This is more math-heavy — good if you're aiming for research or you like knowing the "why" behind algorithms.

You want some books?
4) *Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow — Aurélien Géron*
Super practical. Helped me go from “I get the concepts” to actually training models and cleaning data.

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