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https://github.com/ayushai/machine-learning-notes

Machine Learning Notes - A comprehensive repository featuring my handwritten notes and code files on machine learning. Explore topics like supervised and unsupervised learning, deep learning, and model evaluation. Perfect for students, professionals, and enthusiasts looking to deepen their understanding.
https://github.com/ayushai/machine-learning-notes

datapreprocessing datawrangling linear-regression logistic-regression machine-learning-algorithms

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Machine Learning Notes - A comprehensive repository featuring my handwritten notes and code files on machine learning. Explore topics like supervised and unsupervised learning, deep learning, and model evaluation. Perfect for students, professionals, and enthusiasts looking to deepen their understanding.

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README 

        
# Machine Learning Notes



Welcome to my **Machine Learning Notes** repository! This repo is a curated collection of my handwritten notes and code files, designed to help you dive deep into the world of machine learning.



## What's Inside



### Handwritten Notes

I’ve compiled detailed handwritten notes that cover a wide range of machine learning topics. These notes are crafted to break down complex concepts into easy-to-understand explanations. Whether you're revising for an exam, preparing for an interview, or just learning for fun, these notes can serve as a valuable resource.



### Code Files

In addition to notes, you'll find code files that correspond to various machine learning algorithms and techniques. These are practical implementations meant to reinforce the theoretical concepts. Each code file is well-commented to help you understand the logic and workflow.



## Topics Covered

Here’s a sneak peek into some of the topics covered:



- **Supervised Learning**: Linear Regression, Logistic Regression, Decision Trees, Random Forests, etc.

- **Unsupervised Learning**: K-Means Clustering, Principal Component Analysis (PCA), etc.

- **Deep Learning**: Neural Networks, CNNs, RNNs, etc.

- **Model Evaluation**: Accuracy, Precision, Recall, F1-Score, Confusion Matrix, etc.

- **Feature Engineering**: Feature Scaling, Encoding, Selection, etc.



## How to Use This Repo

- **Browse the Notes**: Feel free to explore the handwritten notes in the `Notes` folder. Each note is organized by topic for easy navigation.

- **Run the Code**: The `Code` folder contains Jupyter notebooks and Python scripts. You can clone the repo and run these on your local machine to practice.

- **Learn and Contribute**: This repository is a work in progress. If you find something missing or have suggestions, feel free to contribute by opening an issue or submitting a pull request.



## Why This Repo?

Machine learning can be a daunting subject, and sometimes, traditional resources don’t quite cut it. My goal with this repository is to provide a blend of theory and practice that can help bridge the gap between understanding concepts and applying them effectively.



## Get Started

1. **Clone the Repository**: `git clone https://github.com/ayushai/machine-learning-notes.git`

2. **Explore and Learn**: Dive into the notes and code, and start enhancing your machine learning knowledge.



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Thank you for checking out my Machine Learning Notes repository. Whether you're a beginner or an experienced practitioner, I hope you find this resource helpful. Happy learning!