IBM AI Engineering Professional Course - Study Repository

Welcome to the IBM AI Engineering Professional Course study repository!
This project contains all source code, notebooks, and resources created and collected during the study of the IBM AI Engineering Professional Certificate program.

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📚 About This Repository

This repository is organized by course modules and covers a wide range of topics in AI, machine learning, deep learning, and generative AI. Each folder corresponds to a specific module or project from the IBM AI Engineering curriculum.

Main Topics Covered

• Machine Learning with Python
• Deep Learning with Keras and TensorFlow
• Neural Networks and PyTorch
• AI Capstone Projects
• Generative AI and LLMs
• RAG (Retrieval-Augmented Generation) and LangChain
• NLP & Language Understanding
• Fine-Tuning Transformers

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📁 Repository Structure

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├── 01. Machine Learning with Python/
├── 02. Introduction to Deep Learning & Neural Networks with Keras/
├── 03. Deep Learning with Keras and Tensorflow/
├── 04. Introduction to Neural Networks and PyTorch/
├── 05. Deep Learning with PyTorch/
├── 06. AI Capstone Project with Deep Learning/
├── 07. Generative AI and LLMs Architecture and Data Preparation/
├── 08. Gen AI Foundational Models for NLP & Language Understanding/
├── 09. Generative AI Language Modeling with Transformers/
├── 10. Generative AI Engineering and Fine-Tuning Transformers/
├── 11. Generative AI Advance Fine-Tuning for LLMs/
├── 12. Fundamentals of AI Agents Using RAG and LangChain/
├── 13. (Project) Generative AI Applications with RAG and LangChain/
├── pyproject.toml
├── uv.lock
├── .python-version
├── .gitignore
└── README.md

• Each module contains Jupyter notebooks (.ipynb), scripts, and supporting files.
• Projects and capstone assignments are included in their respective folders.

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🚀 Getting Started

1. Clone the repository:

   git clone https://github.com/ngoc-minh-do/IBM-AI-Engineering-Professional.git
   cd IBM-AI-Engineering-Professional

2. Set up the Python environment:

   • Python version is specified in .python-version.
   • Use uv for dependency management:

     uv sync
     uv run jupyter notebook

   • For detailed setup instructions, see this guide.

3. Open notebooks:

   • Use Visual Studio Code or Jupyter Notebook to explore and run the notebooks.

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🛠️ Requirements

• Python 3.12+
• uv
• Jupyter Notebook
• See pyproject.toml for all dependencies

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📖 References

• IBM AI Engineering Professional Certificate
• IBM watsonx.ai Documentation
• LangChain Documentation

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📝 License

This repository is for educational purposes only.
Notebooks and code are based on IBM course materials and public datasets.

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🙏 Acknowledgements

Thanks to IBM and all course instructors for providing high-quality learning resources and hands-on labs.

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