ML-Basics

Welcome to the ML-Basics repository! This project is designed for personal learning and exploration of fundamental machine learning concepts. It covers a variety of topics, from basic data preprocessing to implementing different machine learning algorithms using popular libraries like Scikit-learn, TensorFlow, and PyTorch.

Table of Contents

• Introduction
• Getting Started
  • Prerequisites
  • Installation
• Directory Structure
• Topics Covered
  • Data Preprocessing
  • Supervised Learning
  • Unsupervised Learning
  • Neural Networks
  • Model Evaluation
• Usage
• Contributing
• License
• Contact

Introduction

This repository serves as a comprehensive guide for anyone starting out in machine learning. It includes step-by-step tutorials, code examples, and detailed explanations of various ML techniques and algorithms.

Getting Started

Prerequisites

To get the most out of this repository, you should have a basic understanding of Python programming and some familiarity with statistics and linear algebra. Additionally, you will need the following software installed:

• Python 3.7 or higher
• Jupyter Notebook
• Git

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/ML-Basics.git

2. Navigate to the project directory:

   cd ML-Basics

3. Create a virtual environment:

   python -m venv env

4. Activate the virtual environment:
   • On Windows:

     .\env\Scripts\activate

   • On macOS and Linux:

     source env/bin/activate

5. Install the required packages:

   pip install -r requirements.txt

Directory Structure

ML-Basics/
│
├── data/
│   └── datasets/       # Sample datasets for practice
│
├── notebooks/
│   ├── 01_data_preprocessing.ipynb
│   ├── 02_supervised_learning.ipynb
│   ├── 03_unsupervised_learning.ipynb
│   ├── 04_neural_networks.ipynb
│   └── 05_model_evaluation.ipynb
│
├── scripts/
│   └── utils.py        # Utility functions
│
├── .gitignore
├── LICENSE
├── README.md
└── requirements.txt

Topics Covered

Data Preprocessing

• Handling missing values
• Feature scaling and normalization
• Encoding categorical variables

Supervised Learning

• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forests
• Support Vector Machines

Unsupervised Learning

• K-means Clustering
• Hierarchical Clustering
• Principal Component Analysis (PCA)

Neural Networks

• Introduction to neural networks
• Building neural networks with TensorFlow and Keras
• Training and evaluating neural networks

Model Evaluation

• Cross-validation
• Confusion matrix
• ROC curves and AUC
• Precision, recall, and F1 score

Usage

To run the Jupyter Notebooks:

1. Start Jupyter Notebook:

   jupyter notebook

2. Open the desired notebook from the notebooks directory.

Contributing

Contributions are welcome! If you have any improvements or additional content you'd like to add, please fork the repository and submit a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.