HandsOn-ML-Basics

A hands-on approach to learning machine learning, with practical examples to grasp essential concepts.

Machine Learning Note: ML Basics Notion

· Report Bug · Request Feature

---

Table of Contents

1. About The Project
• Core Features
• Built With
2. Getting Started
• Prerequisites
• Installation & Setup
• Project Structure
3. Resources
4. Contact

---

About The Project

HandsOn-ML-Basics is a basic-level repository crafted to help developers and students master the core concepts of machine learning through practical, hands-on coding examples. Each folder is organized as a module focusing on foundational and somewhat advanced topics, following the typical academic and industry ML curriculum.

Whether you are a beginner or want to reinforce your understanding of machine learning essentials, this repository will provide you with ready-to-run Jupyter notebooks, detailed explanations, and real-world scenarios.

Core Features

• Comprehensive Modules
  Structured by topic, covering the progression from foundational supervised learning to clustering and unsupervised learning.

• Hands-On Jupyter Notebooks
  Each concept is illustrated with practical examples, datasets, and clear code explanations.

• Industry Best Practices
  Notebook code adheres to professional conventions, with comments and markdown cells for clarity.

• Easy Navigation
  Folder-wise organization by concept for a streamlined learning experience.

(back to top)

---

Built With

• Python – The language of choice for machine learning
• Jupyter Notebook – Interactive coding environment for experimentation and visualization
• NumPy, Pandas, scikit-learn – Core ML/data science libraries

(back to top)

---

Getting Started

Follow these steps to set up the HandsOn-ML-Basics repository and begin exploring the examples.

Prerequisites

• Python (3.10+ recommended)
• Jupyter Notebook
• Git (to clone the repository)

You can install required Python libraries using the following command:

pip install numpy pandas scikit-learn jupyter

Installation and Setup

1. Clone the repository:

   git clone https://github.com/gayanukabulegoda/HandsOn-ML-Basics.git
   cd HandsOn-ML-Basics

2. Open Jupyter Notebook:

   jupyter notebook

3. Navigate to a module folder and open the relevant notebook:

   • Each module is organized in a separate folder (e.g., 01] Foundational Supervised Learning Concepts/).

(back to top)

---

Project Structure

A simplified overview of the directory structure:

.
├── 01_Foundational_Supervised_Learning_Concepts/
│   └── ... (notebooks & datasets)
├── 02_Supervised_Learning_and_Model_Optimization/
│   └── ... (notebooks & datasets)
├── 03_Supervised_Learning_Classification/
│   └── ... (notebooks & datasets)
├── 04_Unsupervised_Learning_Clustering/
│   └── ... (notebooks & datasets)
├── README.md
└── ...

• Each folder corresponds to a specific topic/module.
• Jupyter notebooks inside demonstrate concepts with code examples and explanations.

Folder	Topic
01	Foundational Supervised Learning Concepts
02	Supervised Learning and Model Optimization
03	Supervised Learning - Classification
04	Unsupervised Learning - Clustering

(back to top)

---

Resources

• ML Basics Notion Notes
  In-depth notes accompanying this repository, with additional theory, explanations, and reference material.

• scikit-learn User Guide
  Official documentation for the main ML library used in examples.

• Google Machine Learning Crash Course
  Interactive online course with video lectures and exercises.

• Kaggle Learn: Intro to Machine Learning
  Practical, hands-on ML tutorials and exercises.

(back to top)

---

Contact

• Repository Link: HandsOn-ML-Basics
• Email: grbulegoda@gmail.com
• Portfolio: https://grbulegoda.me/

(back to top)

---

© 2025 Gayanuka Bulegoda

---