This repository showcases my journey and practical work in Artificial Intelligence and Machine Learning. It contains a curated collection of personal projects, learning implementations, and experimental work that demonstrates my capabilities in modern AI/ML technologies and frameworks.
Purpose: This repository serves as a comprehensive portfolio for professional evaluation, highlighting both original project work and documented learning from industry-leading resources.
Location: AgenticAI/07-langchain/appointment-agent
A sophisticated agentic AI system built with LangChain for managing appointment scheduling workflows. This project demonstrates:
- Multi-agent orchestration and conversation management
- Integration with LangChain's agent framework
- Practical implementation of conversational AI patterns
- Real-world application of AI agents in business workflows
Location: ML
Original machine learning implementations covering various domains and techniques. This collection demonstrates hands-on experience with:
- Supervised and unsupervised learning algorithms
- Model training, evaluation, and optimization
- Data preprocessing and feature engineering
- Practical ML deployment strategies
This repository documents my continuous learning journey through structured courses and self-directed exploration. The implementations here reflect knowledge gained from multiple high-quality sources:
- 🤖 Claude AI - Advanced AI assistance and best practices in prompt engineering
- 🦜 LangChain Academy - Official LangChain framework training and patterns
- 🚢 CrewAI - Multi-agent system orchestration and collaboration
- 📚 Additional Resources - Various online courses, documentation, and community tutorials
The content in this repository falls into two categories:
- Original Projects - Personal implementations and original work including:
- Appointment Agent System
- Machine Learning Projects (ML directory)
- Custom implementations and experiments
- Learning Implementations - Code following tutorials and courses from various sources, adapted and documented for learning purposes
Note: Where applicable, specific learning sources are credited within individual project directories. This repository represents both original work and educational implementations synthesized from various learning resources.
AIML/
├── AgenticAI/
│ ├── 01-langgraph/ # Stateful multi-actor applications
│ ├── 02-ai_protocols/ # AI communication patterns
│ ├── 03-prompt_engineering/ # Advanced prompting techniques
│ ├── 04-aws-cloud-bedrock/ # AWS Bedrock integrations
│ ├── 05-crew_ai/ # Multi-agent orchestration
│ └── 06-langchain/
│ └── appointment-agent/ # Production appointment system
└── ML/ # Machine learning implementations
- Agentic AI Systems - Building autonomous AI agents with decision-making capabilities
- LangChain Framework - Developing LLM-powered applications and chains
- Multi-Agent Orchestration - Coordinating multiple AI agents for complex workflows
- Machine Learning - Classical ML algorithms, model training, and evaluation
- Prompt Engineering - Crafting effective prompts for large language models
- AI Application Architecture - Designing scalable AI-powered systems
To explore the projects in this repository:
-
Clone the repository
git clone https://github.com/saadtechgeek/AIML.git cd AIML -
Navigate to specific projects
# Example: Appointment Agent cd AgenticAI/07-langchain/appointment-agent
-
Follow individual project READMEs for specific setup instructions and requirements
🟢 Active Development - This repository is continuously updated with new projects and learnings
This repository is shared as part of my professional portfolio to demonstrate:
- Practical AI/ML implementation skills
- Ability to learn and adapt to new technologies rapidly
- Understanding of modern AI frameworks and best practices
- Problem-solving approach to real-world AI applications
Interested in discussing these projects or potential collaboration opportunities?
- GitHub: @saadtechgeek
- Repository: AIML Projects
Please refer to individual project directories for specific licensing information. Educational content adapted from courses retains attribution to original creators.