ml-notebook

ML

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
• License

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/PhenomSG/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
   

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

License

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

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