Machine Learning Projects Repository

Welcome to my Machine Learning Projects repository! This repository contains various implementations of machine learning algorithms and methods for solving different types of problems, ranging from classification to regression. Each project is designed to showcase my understanding of different machine learning techniques, model evaluation, and optimization strategies.

Projects Overview

1. Exact Method

The Exact Method project involves implementing exact algorithms to solve specific optimization problems. These methods ensure finding the global optimum solution, typically used in combinatorial optimization tasks.

• Key Concepts: Exact algorithms, Optimization, Global optimum

2. GD Opt (Gradient Descent Optimization)

This project focuses on implementing Gradient Descent for optimization, one of the most popular algorithms in machine learning for minimizing the cost function and training models.

• Key Concepts: Gradient Descent, Optimization, Cost Minimization

3. K-Nearest Neighbors (KNN)

The KNN project implements the K-Nearest Neighbors algorithm, a simple yet powerful method for classification and regression problems.

• Key Concepts: KNN, Distance Metrics, Classification, Regression

4. Neural Network

This project showcases a basic implementation of Neural Networks, focusing on supervised learning tasks like classification. The neural network models can be trained and tested on various datasets to evaluate their performance.

• Key Concepts: Neural Networks, Backpropagation, Activation Functions, Model Training

5. Regression

The Regression project contains implementations of various regression models, including linear regression, polynomial regression, and other advanced techniques to predict continuous values.

• Key Concepts: Linear Regression, Polynomial Regression, Model Evaluation

6. Regularization and Scaling

In this project, I explore the effects of regularization techniques such as L1 and L2 regularization, as well as feature scaling methods to prevent overfitting and improve model performance.

• Key Concepts: Regularization (L1, L2), Feature Scaling, Overfitting Prevention

Installation and Setup

To run any of the projects locally, follow these steps:

1. Clone the Repository:

   git clone https://github.com/samprati96/Machine-Learning.git

2. Install Dependencies: Each project has its own set of dependencies. It's recommended to use a virtual environment.

3. Run the Project: Navigate to the specific project directory and run the code using your preferred IDE or command line.

Contributions

Contributions are welcome! Feel free to open a pull request or submit an issue for any bugs or suggestions.