Welcome to the Data Science Enthusiast's Resource Repository! 🚀 This repository is a comprehensive collection of books, cheatsheets, and code snippets covering the complete lifecycle of data science. Whether you're a beginner or an experienced practitioner, you'll find valuable resources organized into various folders.
- Statistics
- Mathematics
- Python Programming
- Python Libraries
- Data Preprocessing Codes
- Machine Learning
- Deep Learning
- NLP/NLP by Krish Naik
- SQL
- MLOPS
- Transformers
- Others
Explore foundational statistical concepts to understand and interpret data.
Discover mathematical concepts essential for data science applications.
Enhance your Python programming skills with practical examples.
Explore popular Python libraries like Numpy, Pandas, Matplotlib, Seaborn, and Scikit-learn.
Access code snippets for data cleaning, handling missing values, and feature engineering.
Dive into the world of machine learning with algorithms, models, and implementation guides.
Explore neural networks, deep learning architectures, and frameworks.
Get resources related to Natural Language Processing, curated by Krish Naik.
Master SQL for effective data retrieval and manipulation.
Learn about Machine Learning Operations for deploying and managing models.
Explore transformer models and their applications.
Find miscellaneous resources covering various aspects of data science.
We appreciate and acknowledge the contributions from various open sources. Check out the Credits file for details.
Feel free to contribute to this repository by adding more resources, fixing issues, or suggesting improvements. Check the Contribution Guidelines for details.
Happy Learning! 📚✨