A comprehensive resource list covering various topics in Data Science, Machine Learning, and Deep Learning, from beginner to advanced levels.

If you're new to the field, it is recommend starting with th Beginner Roadmap folder. This section guides you through the basics and fundamentals, preparing you for more advanced topics.

For those looking to follow a structured approach:

1. Data Analyst: Learn how to collect, analyze, and visualize data to gain insights.
2. Data Science: Discover the entire data science process, from problem formulation to model deployment.
3. Machine Learning: Master the art of training algorithms to make predictions or decisions based on data.
4. Deep Learning: Dive into the world of neural networks and learn how to build complex models.

Once you have a solid foundation, explore these specialized areas:

1. Natural Language Processing (NLP): Learn how to process, understand, and generate human language.
2. Computer Vision: Discover image and video processing techniques to extract insights from visual data.
3. Reinforcement Learning: Understand how agents learn through trial and error to make decisions in complex environments.
4. Recommendation Systems: Develop models that suggest products or services based on user behavior and preferences.
5. Data Engineering: Learn how to design, build, and maintain large-scale data systems.

• Blogs: Stay up-to-date with the latest news, trends, and insights from industry experts.
• Textbooks: Explore comprehensive books on various topics in Machine Learning, Deep Learning, and Data Science.

This project is open to contributions. If you'd like to add resources, correct mistakes, or suggest new topics, please feel free to submit a pull request.

This project is licensed under the CC0 1.0 Universal license.