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Self-Learning Journey 📘

Welcome to my self-learning GitHub repository! This repo documents my journey of continuous learning and growth in various fields, including programming, algorithms, data structures, artificial intelligence, web development, and more.

Table of Contents

Overview

This repository serves as a collection of the exercises, notes, projects, and code snippets I’ve worked on while learning various concepts and technologies. It’s a living document that grows as I dive deeper into new topics and revisit previously learned material.

Goals

  1. Build a solid foundation in key programming concepts.
  2. Solve algorithmic problems to improve problem-solving skills.
  3. Develop hands-on projects to apply theoretical knowledge.
  4. Explore advanced topics such as AI, computer vision, and data science.
  5. Document my learning process and progress for future reference.

Learning Topics

Some of the key areas covered in this repo: - Programming Languages: JavaScript, Python, etc. - Algorithms and Data Structures: Arrays, Strings, Sliding Window, Dynamic Programming, etc. - Web Development: HTML, CSS, JavaScript, Frontend Frameworks, Backend Development, etc. - Artificial Intelligence: Machine Learning, Deep Learning, Computer Vision, etc. - Projects: Full-stack applications, data analysis tools, AI models, etc.

Project Structure

The repository is organized as follows:

├── docs
│   ├── annotation-platform.md
│   ├── github-jekyl.md
│   └── index.md
├── LICENSE
├── mkdocs.sh
├── mkdocs.yml
├── README.md
└── requirement.txt

Each folder contains relevant code snippets, problem solutions, and notes.

Resources

Here are some of the resources I frequently refer to during my learning: - Books: [Insert book titles] - Online Courses: [Insert course links] - Websites: [Insert websites/blogs] - YouTube Channels: [Insert channels]

How to Use this Repo

  • Browse through the folders based on topics of interest.
  • Check out the projects folder for hands-on implementations.
  • Refer to the notes folder for detailed explanations and documentation.
  • Feel free to clone the repository and use it for your own learning!

Future Plans

  • Dive deeper into advanced data structures and algorithms.
  • Build more full-stack projects.
  • Experiment with AI-based solutions in real-world applications.
  • Expand the repository to include collaborative learning initiatives.

Contributing

This repository is primarily for personal learning, but contributions are welcome! If you find a better solution, have suggestions, or want to share your own learning path, feel free to submit a pull request.

License

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