Introduction:

Welcome to an in-depth exploration of my GitHub repository titled "github.com/JayGangwar07/ML-Algorithms-From-..". This repository is more than just code; it's a learning journey where each algorithm is dismantled and rebuilt from the ground up, providing not just the "how" but the "why" of machine learning.

The Philosophy Behind Scratch Coding

Why implement algorithms from scratch when there are countless libraries and frameworks?

Understanding: By coding from the basics, you gain a profound understanding of how algorithms work at a fundamental level.

Customization: Scratch implementations allow for tailored solutions, especially when dealing with edge cases or specific requirements.

Education: It's an excellent teaching tool, helping others learn by doing, not just by reading.

What's in the Repository?

Here's a brief overview of what you'll find:

Linear Regression: A simple yet powerful model. We've implemented it using the normal equation for small datasets and gradient descent for scalability.

Logistic Regression: For binary classification, we've built this classifier from the sigmoid function up, including both batch and stochastic gradient descent.

K-Nearest Neighbors (KNN): A non-parametric method used for classification and regression. We've coded both versions, focusing on efficiency.

Decision Trees: CART, we've explored different approaches to tree building, focusing on understanding split criteria and tree pruning.

Neural Networks: Starting with a basic perceptron, we've scaled up to single-layer perceptions, explaining forwardpropagation in simple terms.

Learning Path

For someone diving into this repo:

Start Simple: Begin with linear regression or KNN. These algorithms are foundational and provide immediate practical insights.

Iterate for Understanding: Each algorithm includes a README with theoretical explanations, practical examples, and problem-solving approaches.

Experiment: Use the provided Jupyter notebooks to tweak parameters, visualize data, and see how changes affect outcomes.

Contribute: This repo thrives on community input. If you have an optimization or a new algorithm to add, pull requests are welcomed.

Conclusion:

This repository isn't just about seeing algorithms in action; it's about understanding the nuances of machine learning. From debugging to optimization, each code snippet is a lesson in itself. Whether you're a student, a professional looking to brush up on fundamentals, or a curious learner, there's something here for everyone. Dive in, contribute, learn, and most importantly, understand the magic behind machine learning.

Happy Coding!🎉