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@@ -286,3 +286,34 @@ https://www.udacity.com/course/reinforcement-learning--ud600
```
+
+## Rudraksh Parsai
+
+ 
+
+
+[](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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