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Hamoye Data Science Internship Repository

Welcome to my Hamoye Data Science Internship Repository! This repository is dedicated to storing and organizing all the projects, code snippets, and materials I'll be working on during my six-month internship with Hamoye.

Overview

Throughout the internship, I'll be tackling various data science tasks and projects, each residing in its designated folder within this repository. This structure allows for easy navigation and retrieval of specific materials based on the stage of the internship or the project's focus.

Repository Structure

The repository contains different files each representing a phase or stage of the internship. Here's a brief overview of the file structure:

  • Stage A: Contains projects and code developed during the initial stage of the internship: Introduction to Python for Machine Learning.
  • Stage B: Contains materials related to Regression in Machine Learning
  • Stage C: Dedicated to projects and code developed for Classification in Machine Learning.
  • Stage D: Contains resources for Neural Network, Image Recognition & Object Detection.
  • Stage E: Focuses on Practical Time Series Analysis and Forecasting.

How to Navigate

Feel free to explore the repository and folders that interest you the most. Subsequently, updates will be made and each folder will contains its own README.md file providing detailed information about the projects, data used for the project and code within.

Getting Started

If you're new to the repository or looking to get started with the projects, here's a suggested workflow:

  1. Clone the Repository: Start by cloning this repository to your local machine using the following command:

git clone https://github.com/your-username/data-science-internship.git

  1. Navigate to the Desired Phase: Use the folder structure to navigate to the phase or project you're interested in.

  2. Explore the Materials: Once inside a folder, explore the README.md file and other materials available to gain insights into the projects and code.

  3. Contribute (Optional): If you have suggestions, improvements, or feedback, feel free to contribute by opening issues or pull requests.

Feedback and Contact

Your feedback is valuable to me! If you have any questions, suggestions, or just want to connect, feel free to reach out to me via email or LinkedIn.

Thank you for visiting my Hamoye Data Science Internship Repository!

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