Machine Learning Algorithms from Scratch

Welcome to the "Machine Learning Algorithms from Scratch" repository. This repository showcases Python implementations of fundamental machine learning models and algorithms, all constructed from the ground up using libraries such as NumPy and other essential tools. Our aim is to provide clear and instructive examples of these algorithms to deepen your understanding of their inner workings.

Table of Contents

1. About
2. Algorithms Included
3. Usage

About

This project presents Python implementations of pivotal machine learning models and algorithms, all created from scratch. Our primary focus is not on producing highly optimized or computationally efficient code but rather on delivering transparent and accessible implementations that allow for a comprehensive exploration of these algorithms.

Key Features:

• Experiential Learning: This repository serves as a platform for our personal journey in the realm of machine learning. It provides a space to explore and experiment with a wide array of machine learning models and algorithms.

• Algorithmic Depth: Our implementations encompass a diverse range of algorithms, spanning supervised and unsupervised learning, reinforcement learning, deep learning, and more.

• Transparency: Each algorithm is meticulously implemented and thoroughly documented, providing clear insights into their functioning. This resource is designed to facilitate learning and experimentation.

The primary objective of this repository is to foster our personal growth and understanding of machine learning. While it is conceived as a space for personal development, it remains open for others to explore, learn, and potentially collaborate on machine learning projects.

We invite you to delve into the code, run examples, and connect with us if you have questions or ideas to share.

Algorithms Included

Here's a comprehensive list of machine learning algorithms available in this repository, along with brief explanations:

1. Linear Regression - Linear regression is a foundational algorithm for modeling the relationship between dependent and independent variables. It is widely used in statistics and machine learning for tasks such as prediction and forecasting.

2. Logistic Regression - Logistic regression is a fundamental classification algorithm used to model the probability of a binary outcome. It's widely employed in machine learning for tasks such as binary classification and has applications in various domains, including healthcare, finance, and marketing.

3. Naive Bayes - Naive Bayes is a probabilistic classification algorithm based on Bayes' theorem. It assumes that features are independent and is particularly suitable for text classification and spam filtering.

4. KMeans - KMeans is an unsupervised clustering algorithm used to partition data into K clusters based on similarity. It is widely used for data analysis and exploration.

5. Perceptron - Perceptron is a binary linear classifier used for supervised learning of binary classifiers. It is one of the simplest neural network architectures and forms the basis for more complex models.

6. Decision Tree - Decision Tree is a versatile supervised learning algorithm that recursively partitions the data based on feature values, forming a tree-like structure. It's widely used for both classification and regression tasks, providing interpretable and easy-to-understand models suitable for various domains such as finance, healthcare, and marketing.

7. Random Forest - An ensemble learning method that constructs multiple decision trees during training and outputs the class mode or mean prediction of the individual trees. It offers enhanced accuracy and robustness compared to single decision trees and is widely utilized for classification and regression tasks across domains.

Each algorithm is implemented in Python and meticulously documented to facilitate comprehension of both the code and the underlying principles.

Usage

To embark on your journey with this repository, follow these steps:

1. Clone this repository to your local machine:

   git clone https://github.com/aayushsoni4/Machine-Learning-Algos-from-Scratch.git

2. Create a virtual environment using Python's venv module. Replace 'venv-name' with your preferred name:

   python -m venv venv-name

3. Activate the virtual environment:

   • On Windows:

   venv-name\Scripts\activate

   • On macOS and Linux:

   source venv-name/bin/activate

4. Navigate to the cloned repository:

   cd Machine-Learning-Algos-from-Scratch

5. Install the required packages from the requirements.txt file:

   pip install -r requirements.txt

This setup allows you to explore, experiment, and engage with the content of this repository effectively.