https://github.com/narenkhatwani/machine-learning-for-babies
We are trying to simplify some Machine Learning concepts for folks new to this domain with some relatable examples
https://github.com/narenkhatwani/machine-learning-for-babies
Last synced: 2 months ago
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We are trying to simplify some Machine Learning concepts for folks new to this domain with some relatable examples
- Host: GitHub
- URL: https://github.com/narenkhatwani/machine-learning-for-babies
- Owner: narenkhatwani
- Created: 2024-04-21T23:05:10.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-22T22:57:58.000Z (10 months ago)
- Last Synced: 2025-06-19T02:53:34.069Z (4 months ago)
- Size: 40.6 MB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Machine Learning for Babies
Welcome to the **Machine Learning for Babies** repository! Below is an index of the chapters in numerical order to help you navigate through the content.
## Index
1. [Introduction](https://github.com/narenkhatwani/machine-learning-for-babies/tree/main/1.Introduction)
2. [KNN Algorithm](https://github.com/narenkhatwani/machine-learning-for-babies/tree/main/2.KNN%20Algorithm)
3. [Linear Regression](https://github.com/narenkhatwani/machine-learning-for-babies/tree/main/3.Linear%20Regression)
4. [Perceptron](https://github.com/narenkhatwani/machine-learning-for-babies/tree/main/4.Perceptron)
5. [Logistic Regression](https://github.com/narenkhatwani/machine-learning-for-babies/tree/main/5.Logistic%20Regression)
6. [Regularization](https://github.com/narenkhatwani/machine-learning-for-babies/tree/main/6.Regularization)
7. [Support Vector Machine](https://github.com/narenkhatwani/machine-learning-for-babies/tree/main/7.Support%20Vector%20Machine)
8. [Decision Tree, Random Forest](https://github.com/narenkhatwani/machine-learning-for-babies/tree/main/8.Decision%20Tree,%20Random%20Forest)
9. [Feature Selection](https://github.com/narenkhatwani/machine-learning-for-babies/tree/main/9.Feature%20Selection)
10. [Ensembles and Boosting](https://github.com/narenkhatwani/machine-learning-for-babies/tree/main/11.Ensembles%20and%20Boosting)
11. [Probabilistic Perspective](https://github.com/narenkhatwani/machine-learning-for-babies/tree/main/12.Probabilistic%20Perspective)
12. [Dimensionality Reduction, Kernel PCA](https://github.com/narenkhatwani/machine-learning-for-babies/tree/main/13.Dimensionality%20Reduction,%20Kernel%20PCA)
13. [Unsupervised Clustering](https://github.com/narenkhatwani/machine-learning-for-babies/tree/main/14.Unsupervised%20Clustering)
14. [ANNs](https://github.com/narenkhatwani/machine-learning-for-babies/tree/main/15.ANNs)
15. [MLP Classifier](https://github.com/narenkhatwani/machine-learning-for-babies/tree/main/16.MLP%20Classifier)
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## About the Repository
This repository is designed to introduce the basics of machine learning in a simplified and fun way. Whether you're just starting out or brushing up on concepts, you'll find something valuable here!
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I tried learning Machine Learning concepts for a long time but I couldn't find a good resource. Eventually this Fall (Fall 2024) I opted for the Machine Learning course at my university (NJIT) under Prof Khalid Bakhshaliyev. He held a discussion-based class and it was quite good.
One of the key resources he gave us to revise the concepts he taught in class was video-recorded lectures by Dr Yiannis Koutis. My classmate Sarang Patil and I have nicknamed Dr Koutis as the ML legend amongst ourselves because he has provided us with the best possible learnings.
One of the key things while learning Machine Learning or any concepts is to understand the fundamentals of it. Those fundamentals are something that Dr Koutis made us understand in simple terms.
I cannot upload the lectures of Dr Koutis for obvious reasons, but I have made my own notes of what I understood from his lectures and I am uploading them here. If it helps anyone understand any concept in Machine Learning, I and Sarang would be very happy.
Sarang and I will also try to write readme descriptions for all the PDFs, so it's easier for folks out there to relate the notes and theory. AND if Sarang is convinced we would also try to make some loom videos (I hope he agrees to this WILD time-consuming idea of mine).
A funny meme to make this theory look a little interesting:
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## Contributing
Feel free to contribute by adding examples or improving explanations!