ML Algorithm from SCRATCH

Description:

This repository contains the code implementations for machine learning algorithms from scratch, designed to provide a clear understanding of how fundamental machine learning models work. The repository includes various linear regression implementations using different techniques, perfect for learners who want to understand the mathematics and coding behind popular algorithms.

The project is accompanied by tutorial videos and blog posts to guide users through the process of building machine learning models from the ground up. The goal is to demonstrate machine learning concepts in a simple and intuitive wa

Features:

• Linear Regression using Least Squares: A step-by-step implementation of linear regression using the least squares method.
• Linear Regression using Gradient Descent: A version of linear regression built using gradient descent optimization, demonstrating an iterative approach.
• Linear Regression in 2 Minutes: A fast-paced, simplified implementation to understand the core concepts of linear regression in minimal time.
• Tutorial Videos: Each model comes with a tutorial video explaining the theory and coding behind the algorithms.
• Medium Posts: Detailed blog posts accompanying certain models to dive deeper into the implementation and math behind the algorithms.

Requirements:

• Python: 3.x or higher
• Dependencies:
  • numpy – For numerical computations
  • matplotlib – For plotting graphs and visualizations
  • pandas – For handling datasets (if required)
  • scikit-learn (optional) – To compare with pre-built machine learning models

To install the dependencies, you can use:

bash

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pip install numpy matplotlib pandas scikit-learn

Instructions:

1. Clone the Repository: First, clone the repository to your local machine using Git:

   bash

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   git clone https://github.com/<your-username>/machine-learning-from-scratch.git

2. Install Dependencies: This project uses Python and the required libraries should be installed. You can create a virtual environment and install the dependencies using pip:

   bash

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   pip install -r requirements.txt

3. Run the Code: Navigate to the directory where you have cloned the repository and choose the script you want to run. For example:

   bash

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   python linear_regression_least_squares.py

   Follow the tutorial video for instructions on how to execute the code for each model.

4. Check Out the Tutorials:

   • Watch the tutorial videos linked in the respective files for deeper explanations.
   • For written explanations and additional insights, check out the associated Medium blog posts.