https://github.com/rahul-404/full_stack_data_science_with_generative_ai

Welcome to the repository for the course "Full Stack Data Science with Generative AI". 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.
https://github.com/rahul-404/full_stack_data_science_with_generative_ai

data-analysis data-science data-visualization database deep-learning exploratory-data-analysis feature-engineering generative-ai machine-learning nlp python statistics

Last synced: 7 days ago
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Welcome to the repository for the course "Full Stack Data Science with Generative AI". 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.

• Host: GitHub
• URL: https://github.com/rahul-404/full_stack_data_science_with_generative_ai
• Owner: Rahul-404
• License: mit
• Created: 2024-06-19T07:11:09.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-08-23T05:31:12.000Z (over 1 year ago)
• Last Synced: 2025-02-01T22:13:57.269Z (about 1 year ago)
• Topics: data-analysis, data-science, data-visualization, database, deep-learning, exploratory-data-analysis, feature-engineering, generative-ai, machine-learning, nlp, python, statistics
• Language: Jupyter Notebook
• Homepage:
• Size: 1.14 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# Full Stack Data Science with Generative AI

Welcome to the repository for the course "Full Stack Data Science with Generative AI". 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.

## Course Curriculum

This 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:

1. **Python**

   - Basics of Python programming language

   - Python projects and applications

2. **Data Manipulation and Analysis**

   - Pandas for data manipulation

   - NumPy for numerical computations

3. **Data Visualization**

   - Visualization libraries (e.g., Matplotlib, Seaborn)

4. **Databases**

   - SQL fundamentals

   - NoSQL with MongoDB

5. **Statistics**

   - Basic statistics

   - Advanced statistics

6. **Feature Engineering**

   - Techniques for feature extraction and selection

7. **Exploratory Data Analysis (EDA)**

   - Methods for data exploration and insights

8. **Machine Learning**

   - Introduction to machine learning concepts

   - Supervised learning algorithms

   - Unsupervised learning techniques

   - Time series analysis

9. **Natural Language Processing (NLP)**

   - NLP fundamentals for machine learning applications

10. **End-to-End Machine Learning Projects**

    - Integration of data preprocessing, model building, and deployment

11. **Interview Preparation**

    - Tips and resources for preparing for data science and ML interviews

12. **Deep Learning**

    - Introduction to deep learning concepts

    - Deep learning for computer vision

    - Deep learning for NLP

13. **Generative AI**

    - Introduction to generative AI techniques

    - Overview of OpenAI and its ready-to-use models with applications

14. **Advanced Topics**

    - Prompt engineering with OpenAI

    - Vector databases with Python for large language model (LLM) use cases

    - Hands-on with LangChain

    - Practical guide to LlamaIndex with LLMs

15. **GenAI End-to-End Projects**

    - Implementation of generative AI projects from start to finish

## Repository Structure

- **Lectures**: Contains lecture notes, slides, and supplementary materials.

  

- **Exercises**: Hands-on exercises and assignments to reinforce learning.

  

- **Projects**: Capstone projects and real-world applications using generative AI techniques.

  

- **Resources**: Additional resources, references, and links for further exploration.

## Getting Started

To get started with the course, clone this repository to your local machine:

```bash

git clone https://github.com/Rahul-404/Full_Stack_Data_science_with_Generative_AI.git

```

Make sure to install any necessary dependencies outlined in the course materials and follow along with the provided exercises and projects.

## Contributing

Contributions 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.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Acknowledgments

- Mention any acknowledgments or credits to individuals or organizations whose work or tools you are using in this course.