Welcome to the official repository for "Mastering AI Tools for Development" — your gateway to mastering the tools, platforms, and frameworks that power modern AI/ML workflows. This course is perfect for IT professionals and developers who want to build and deploy AI solutions using real-world toolkits.

This hands-on course is structured into seven core segments, each focusing on essential tools and concepts in the AI/ML ecosystem:

• Instructor: Rob Barton
• Topics:
  • Foundational tools
  • Exploring frameworks
  • Working with GPUs

• Instructor: Rob Barton
• Topics:
  • Introduction to Jupyter
  • Setup and usage
  • Tips and tricks for AI/ML projects

• Instructor: Jerome Henry
• Topics:
  • Python crash course
  • Introduction to Scikit Learn

• Instructor: Jerome Henry
• Topics:
  • R and R Studio
  • Matlab and Octave

• Instructor: Jerome Henry
• Topics:
  • TensorFlow and PyTorch
  • Model selection
  • Embedded systems: MicroPython, TensorFlow Lite, Edge Impulse

• Instructor: Rob Barton
• Topics:
  • Amazon SageMaker and Azure AI
  • Jupyter Notebook instances
  • Model training, tuning, and deployment

• Instructor: Rob Barton
• Topics:
  • LLM use cases
  • Auto-Encoding vs. Auto-Regressive LLMs
  • Open-Source vs. Closed-Source LLMs
  • Hugging Face Transformers
  • Model Context Protocol (MCP)

├── Segment1_AI_ML_Landscape/    # Framework overviews and GPU demo scripts
├── Segment2_Jupyter/            # Jupyter usage examples and tips
├── Segment3_Scikit_Learn/       # Python basics and Scikit Learn notebooks
├── Segment4_Statistics_Tools/   # Scripts and notes for R, MATLAB, Octave
├── Segment5_Deep_Learning/      # TensorFlow/PyTorch examples and ANN experiments
├── Segment6_Cloud_Development/  # Cloud model development and deployment
└── Segment7_LLMs_and_MCP/       # Hugging Face, ChatGPT demos, and MCP workflows

Each segment includes:

• 📓 Jupyter Notebooks (.ipynb) or Python scripts (.py)
• 📊 Data files and sample outputs (where applicable)
• 📝 Section-specific README files or inline documentation

Before you begin, ensure you have:

• 🐍 Python 3.8 or higher
• 📚 Jupyter Notebook or JupyterLab
• 🔧 Git installed
• 💻 Basic Python programming knowledge

1. Clone the repository:

   git clone https://github.com/robbarto2/AI-Tools.git
   cd AI-Tools

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Start learning! Open and run the Jupyter notebooks or Python files for each section.