https://github.com/geonextgis/end-to-end-machine-learning

Welcome to the "End-to-End Machine Learning" repository, your comprehensive guide to mastering the entire lifecycle of machine learning projects.
https://github.com/geonextgis/end-to-end-machine-learning

Last synced: 3 days ago
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Welcome to the "End-to-End Machine Learning" repository, your comprehensive guide to mastering the entire lifecycle of machine learning projects.

• Host: GitHub
• URL: https://github.com/geonextgis/end-to-end-machine-learning
• Owner: geonextgis
• Created: 2023-08-25T14:02:09.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-06-22T11:01:34.000Z (7 months ago)
• Last Synced: 2024-11-24T22:12:30.367Z (2 months ago)
• Language: Jupyter Notebook
• Size: 11.3 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# **End to End Machine Learning**

Welcome to the "End to End Machine Learning" repository! This curated collection offers an extensive array of original Jupyter Notebooks, meticulously crafted to delve into diverse facets of machine learning. Whether you're a novice eager to grasp the fundamentals or a seasoned practitioner in search of advanced techniques, this repository serves as your comprehensive guide to immersive, hands-on learning experiences. Explore and enhance your understanding of machine learning concepts, algorithms, and applications through interactive and engaging tutorials.

## Table of Contents

1. [Data Gathering](#00_Data_Gathering)

2. [Exploratory Data Analysis](#01_Exploratory_Data_Analysis_(EDA))

3. [Feature Engineering](#02_Feature_Engineering)

4. [Machine Learning Algorithms](#03_Machine_Learning_Algorithms)

## Introduction

This repository is designed to be a comprehensive tutorial collection, guiding you through the intricacies of machine learning. Each notebook is crafted to explain concepts, provide practical examples, and encourage interactive learning. Whether you are interested in classical machine learning algorithms, deep learning architectures, or deployment strategies, you'll find a notebook tailored to your needs.

## Features

- **Diverse Topics:** Covering a wide range of machine learning concepts, algorithms, and applications.

- **Interactive Learning:** Jupyter Notebooks provide an interactive environment for experimenting with code.

- **Original Content:** Authored with care to ensure clarity and originality in explanations and examples.

- **Documentation:** Clear explanations, step-by-step instructions, and visualizations to enhance understanding.

  

## License

This project is licensed under the [MIT License](LICENSE). Share, learn, and enjoy the journey of machine learning! 📊🚀