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Examples used in the AI-Tools Course

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# 🚀 Mastering AI Tools for Development

![Python Version](https://img.shields.io/badge/python-3.8%2B-blue)
![License](https://img.shields.io/badge/license-MIT-green)
![Course Duration](https://img.shields.io/badge/duration-4%20hours-orange)
![Status](https://img.shields.io/badge/status-active-success)

*A comprehensive 4-hour live training session developed in partnership with Pearson O'Reilly*

</div>

## 👋 Welcome

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.

## 🎯 What You'll Learn

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

### 📚 Course Curriculum

#### 1. Introduction to the AI/ML Landscape
- **Instructor:** Rob Barton
- **Topics:**
  - Foundational tools
  - Exploring frameworks
  - Working with GPUs

#### 2. Jupyter Notebooks
- **Instructor:** Rob Barton
- **Topics:**
  - Introduction to Jupyter
  - Setup and usage
  - Tips and tricks for AI/ML projects

#### 3. Python for AIML with Scikit Learn
- **Instructor:** Jerome Henry
- **Topics:**
  - Python crash course
  - Introduction to Scikit Learn

#### 4. Mathematics and Statistics-Oriented Tools
- **Instructor:** Jerome Henry
- **Topics:**
  - R and R Studio
  - Matlab and Octave

#### 5. Deep Learning Tools
- **Instructor:** Jerome Henry
- **Topics:**
  - TensorFlow and PyTorch
  - Model selection
  - Embedded systems: MicroPython, TensorFlow Lite, Edge Impulse

#### 6. Cloud Development Tools
- **Instructor:** Rob Barton
- **Topics:**
  - Amazon SageMaker and Azure AI
  - Jupyter Notebook instances
  - Model training, tuning, and deployment

#### 7. ChatGPT and Other Large Language Model Tools
- **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)

## 📁 Repository Structure

```
├── 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

## ⚙️ Prerequisites

Before you begin, ensure you have:

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

## 🚀 Getting Started

1. **Clone the repository:**
   ```bash
   git clone https://github.com/robbarto2/AI-Tools.git
   cd AI-Tools
   ```

2. **Create and activate a virtual environment:**
   ```bash
   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   ```

3. **Install dependencies:**
   ```bash
   pip install -r requirements.txt
   ```

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

## 👥 Meet the Instructors

### Rob Barton
**Distinguished Engineer, AI & Networking**

### Jerome Henry
**Distinguished Engineer, Wireless & AI**

</div>

---

Made with ❤️ by Rob Barton and Jerome Henry

</div>

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