{"id":22482308,"url":"https://github.com/rahul-404/full_stack_data_science_with_generative_ai","last_synced_at":"2026-04-12T17:04:08.805Z","repository":{"id":245072282,"uuid":"817175245","full_name":"Rahul-404/Full_stack_Data_Science_with_Generative_AI","owner":"Rahul-404","description":"Welcome to the repository for the course \"Full Stack Data Science with Generative AI\". 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This repository is designed to accompany the course and provide resources, exercises, and projects related to the study of data science and generative AI techniques.\n\n## Course Curriculum\n\nThis course covers a comprehensive range of topics essential for mastering full stack data science with a focus on generative AI. Below is an outline of the curriculum:\n\n1. **Python**\n   - Basics of Python programming language\n   - Python projects and applications\n\n2. **Data Manipulation and Analysis**\n   - Pandas for data manipulation\n   - NumPy for numerical computations\n\n3. **Data Visualization**\n   - Visualization libraries (e.g., Matplotlib, Seaborn)\n\n4. **Databases**\n   - SQL fundamentals\n   - NoSQL with MongoDB\n\n5. **Statistics**\n   - Basic statistics\n   - Advanced statistics\n\n6. **Feature Engineering**\n   - Techniques for feature extraction and selection\n\n7. **Exploratory Data Analysis (EDA)**\n   - Methods for data exploration and insights\n\n8. **Machine Learning**\n   - Introduction to machine learning concepts\n   - Supervised learning algorithms\n   - Unsupervised learning techniques\n   - Time series analysis\n\n9. **Natural Language Processing (NLP)**\n   - NLP fundamentals for machine learning applications\n\n10. **End-to-End Machine Learning Projects**\n    - Integration of data preprocessing, model building, and deployment\n\n11. **Interview Preparation**\n    - Tips and resources for preparing for data science and ML interviews\n\n12. **Deep Learning**\n    - Introduction to deep learning concepts\n    - Deep learning for computer vision\n    - Deep learning for NLP\n\n13. **Generative AI**\n    - Introduction to generative AI techniques\n    - Overview of OpenAI and its ready-to-use models with applications\n\n14. **Advanced Topics**\n    - Prompt engineering with OpenAI\n    - Vector databases with Python for large language model (LLM) use cases\n    - Hands-on with LangChain\n    - Practical guide to LlamaIndex with LLMs\n\n15. **GenAI End-to-End Projects**\n    - Implementation of generative AI projects from start to finish\n\n## Repository Structure\n\n- **Lectures**: Contains lecture notes, slides, and supplementary materials.\n  \n- **Exercises**: Hands-on exercises and assignments to reinforce learning.\n  \n- **Projects**: Capstone projects and real-world applications using generative AI techniques.\n  \n- **Resources**: Additional resources, references, and links for further exploration.\n\n## Getting Started\n\nTo get started with the course, clone this repository to your local machine:\n\n```bash\ngit clone https://github.com/Rahul-404/Full_Stack_Data_science_with_Generative_AI.git\n```\n\nMake sure to install any necessary dependencies outlined in the course materials and follow along with the provided exercises and projects.\n\n## Contributing\n\nContributions are welcome! If you find any issues or have suggestions for improvement, please submit an issue or a pull request. For major changes, please open an issue first to discuss what you would like to change.\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## Acknowledgments\n\n- Mention any acknowledgments or credits to individuals or organizations whose work or tools you are using in this course.\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frahul-404%2Ffull_stack_data_science_with_generative_ai","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frahul-404%2Ffull_stack_data_science_with_generative_ai","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frahul-404%2Ffull_stack_data_science_with_generative_ai/lists"}