SM-ML

Welcome to the SM-ML repository. This repository contains notes, resources, and implementations for various Machine Learning topics.

Table of Contents

• Lesson 01 - Introduction
• Lesson 02 - Data Preprocessing
• Lesson 03 - Regression
• Lesson 04 - Classification
• Lesson 05 - Clustering
• Lesson 06 - Association Rule Learning
• Lesson 07 - Reinforcement Learning
• Lesson 08 - Natural Language Processing
• Lesson 09 - Deep Learning
• Lesson 10 - Dimensionality Reduction
• Contributing

Lesson 01 - Introduction

Overview of machine learning, its history, applications, and the structure of this repository.

Lesson 02 - Data Preprocessing

Learn about data cleaning, transformation, and feature engineering techniques.

Lesson 03 - Regression

Explore various regression techniques and their applications.

Lesson 04 - Classification

Dive into classification algorithms and their use cases.

Lesson 05 - Clustering

Understand clustering methods and how to apply them.

Lesson 06 - Association Rule Learning

Learn about discovering interesting relations between variables in large databases.

Lesson 07 - Reinforcement Learning

Introduction to reinforcement learning and its practical applications.

Lesson 08 - Natural Language Processing

Explore the techniques to process and analyze textual data.

Lesson 09 - Deep Learning

Delve into deep learning models and architectures.

Lesson 10 - Dimensionality Reduction

Techniques to reduce the number of random variables in a dataset.

Contributing

Contributions are welcome! If you have any improvements, suggestions, or additional notes to add, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Commit your changes (git commit -m 'Add new notes').
4. Push to the branch (git push origin feature-branch).
5. Create a new Pull Request.