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Machine and Deep Learning Practice Repository With Python

Welcome to my Machine and Deep Learning practice repository! Here, you'll find Python code examples and projects that I've worked on as part of my learning journey in the field of machine and deep learning. These code samples are based on exercises and examples from various books, courses, and online resources.

Folder Structure

  • code/: This directory contains Python code files organized by topics or projects.
  • data/: You can store datasets or data files used in the code here (if applicable).
  • notebooks/: Jupyter/python notebooks for experiments, explanations, or visualizations.
  • resources/: Any additional resources like PDFs of relevant book chapters or reference materials.

Table of Contents

Getting Started

If you want to explore the code and projects in this repository, follow these steps:

  1. Clone this repository to your local machine:
    git clone https://github.com/LaxmanChaudhary1/Machine-and-Deep-Learning-Practice-With-Python-Repository/
    

Code Examples

In the code/ directory, you will find Python code files covering various machine and deep learning topics. These examples are intended to provide practical insights and can be used as reference material for learning and building your own projects.

Projects

In the projects/ directory, you will find more extensive machine and deep learning projects. These projects may include multiple code files, datasets, and Jupyter notebooks. Feel free to explore and use these projects as a basis for your own work.

Contributing

If you'd like to contribute to this repository by adding more code examples, fixing issues, or improving documentation, please follow these steps:

1.Fork this repository.

2.Create a new branch for your changes: git checkout -b feature/your-feature-name 3.Make your changes and commit them: git commit -m "Add your commit message here" 4.Push your changes to your forked repository: git push origin feature/your-feature-name

Lisense

This repository is licensed under the MIT License. See the LICENSE file for details.

Learning Resources

I've used code and exercises from the following book:

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road to ML from python

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