ML-Basics

This repository contains Jupyter Notebooks for the basics of Machine Learning. Each notebook is self-contained and covers a specific topic in detail.

Contents:

1. Linear Regression
2. Logistic Regression
3. Decision Trees
4. Random Forest
5. K-Nearest Neighbors
6. Support Vector Machines
7. K-Means Clustering
8. Singular Value Decmposition

Requirements

The notebooks are written in Python and make use of the following libraries:

1. Numpy
2. Pandas
3. Matplotlib
4. Seaborn
5. Sklearn It is recommended to have a basic understanding of Python and these libraries before diving into the notebooks.

Usage

Clone the repository to your local machine.

bash Copy code git clone https://github.com/gaurav639/Machine_Learning_Basics.git

Open the notebooks using Jupyter Notebook or Jupyter Lab.

Contributions

Contributions in the form of pull requests and suggestions for improvement are always welcome. If you find any errors or have suggestions, please feel free to open an issue.