Hands-On Agile Coaching | Azure Data Engineer With Microsoft Fabric & Data Analytics

Course Overview

The Hands-on Advanced Course on Generative AI & Agentic AI (HAC) is designed to provide a deep, practical understanding of modern Artificial Intelligence systems, focusing on Generative AI, Large Language Models (LLMs), and Agentic AI.

Through a structured and hands-on learning approach, participants will explore foundational AI concepts, LLM architectures, transformers, prompt engineering, LangChain, RAG pipelines, vector databases, and multi-agent systems.

The course emphasizes practical implementation, enabling learners to build, customize, and deploy real-world AI solutions using cutting-edge frameworks like OpenAI, Hugging Face, LangChain, LLaMA, Groq, Agno, and Chromadb.

Ignite your Generative AI career with HAC. Enroll today and embark on a transformative learning journey!

Course Objectives

This course aims to equip learners with a strong foundation and hands-on expertise in Generative AI, Large Language Models, and Agentic AI. Participants will explore the inner workings of LLMs, master prompt engineering, build intelligent AI agents, and implement advanced pipelines like RAG and MCP. The focus is on developing practical, ethical, and scalable AI solutions using modern tools such as LangChain, LLaMA, Groq, Agno, and Chromadb.

Get a deep dive into essential subjects like Azure Cosmo DB, Azure Synapse, Azure Databricks, Azure Stream Analytics, Azure HDInsight, and Azure Data Factory.
Understand the fundamentals and evolution of AI & Generative AI.
Learn the architecture and working of Large Language Models (LLMs).
Master transformers, tokenization, and attention mechanisms.
Design effective prompts and optimize prompt performance.
Build LangChain-based AI workflows and pipelines.
Work with vector databases and similarity search using Chromadb.
Implement Retrieval-Augmented Generation (RAG) systems.
Develop intelligent single and multi-agent systems using Agentic AI.
Fine-tune and evaluate AI models with LoRA and Agno frameworks.
Apply responsible and ethical AI practices in real-world projects.

Hurry up and join our Microsoft Azure Data Engineer Certification Course today to propel your career to greater heights.

Key Topics Covered :

Module 1: AI & Gen AI Fundamentals
- Introduction to AI
- Introduction to Gen AI
- Generative AI Evolution
- Traditional AI vs Gen AI
- Types of Generative AI
- Real-world Applications of Gen AI
- Popular Generative AI Tools and Platforms
- Generative AI glossary
Module 2: Large Language Models (LLMs)
- Introduction to LLMs
- How LLMs work?
- Foundation Models vs LLMs
- Proprietary and Open Source LLMs
- Popular LLMs: GPT, BERT, Claude, LLaMA
- LLMs Architecture: Transformers
- Encoder & Decoder
- Positional Encoding
- Self-Attention
- Training and Fine-Tuning LLMs
Module 3: Transformers:
- Transformers Architecture
- Word & Contextual Embeddings
- Positional embeddings
- Encoder & Decoder
- Tokenization
- Attention Mechanism
- Popular Transformers: Bert, GPT, CoPilot
- Hugging Face: Spam Classification
- Hugging Face: Word Predictions
Module 4: Prompt Engineering
- Introduction to prompt engineering
- Types of prompts
- Instruction-based prompts
- Chain-of-thought prompts
- Few-shot and zero-shot prompts
- Advanced techniques for designing effective prompts
- Evaluating prompt performance and bias
- Prompt optimization tools and techniques
- Hands-on: zero-shot, one-shot, and few-shot prompts
Hands-on: zero-shot, one-shot, and few-shot prompts
- Introduction to Langchain
- Langchain ecosystem
- OpenAI models in Langchain
- Hugging Face models in Langchain
- Langchain Installation
- Groq & LLama setup
- LLM using Langchain
- Prompt Templates & chains
- Parser
- Hands-on Langchain Project
Module 6: Vector databases
- Introduction to Vector databases
- Traditional databases vs Vector databases
- Differences in data storage and retrieval
- Indexing and similarity search
- High dimensional vector space
- Chromadb vector database
- Working with Chromadb
- Chromadb operations
- Distance metrics: cosine similarity, dot product, Euclidean
- Add, update, delete, query operations
- Metadata filtering
Module 7: Retrieval Augmented Generation (RAG)
- Introduction to RAG
- How RAG works?
- RAG vs traditional LLM generation
- Two-phase Architecture: Retrieval + Generation
- Document loaders
- Text splitters
- Retrieval & Answer generation
- Streamlit UI
- Use of Embedding models
- Building a RAG based pipeline
Module 8: AI Agents & Agentic AI
- Introduction to AI agents
- Single-agent vs. multi-agent systems
- Introduction Agentic AI
- How Agentic AI works
- AI Agents vs Agentic AI
- Real-world applications
- Building your first Agent using Llama & Agno
- Reasoning models
- Building Reasoning model using Agno
- Multi-modal Agents (text, image, video)
- Other frameworks: Google ADK, Smolagents
Module 9: Model Context Protocol (MCP)
- Introduction to MCP
- Prebuilt MCP Servers
- A2A Protocol
- Build your first MCP Server
Module 10: Multi-Agent Systems
- Considerations of Multi-Agent systems
- Design Patterns for Multi-Agent Systems
- Route Agent
- Building Multi-Agent systems
- Build multilingual applications using translation + language detection
- File parsing, OCR, and vector search
Module 11: Agentic AI – Evaluation & Fine-Tuning
- Introduction to Agentic AI Evaluation
- Functional Evaluation using Agno
- Safety and Guardrails
- Operational Metrics
- Performance evaluation using Agno
- Fine-Tuning basics
- Low Rank Adaptation (LoRA)
- Quantization (QLoRA)
- Fine tuning Llama with Unsloth
Module 12: Ethics in Gen AI/ Responsible (Gen) AI
- Ethical challenges
- Misinformation & Hallucination
- Bias
- Pil & Privacy laws
- Copyrights & Intellectual Property
- Environmental Impact

Real-time Projects

Project 1: end-to-end RAG project
Project 2: E-commerce chatbot agent
Project 3: Multi-Agent System
Project 4: HR Management System
Project 5: Call Center Agent
Project 6: Personal AI Agent
Project 7: Azure OpenAI & Dall E models
Project 8: Building AI Agents with Azure

Bonuses

Interview Prep
Placement Assistance
Resume/CV Preparation
LinkedIn Profile Optimization

Bonus Module 1: Generative AI on Azure Cloud

Azure AI Fundamentals (AZ900)
Building Gen AI models using Azure OpenAI
Building Chat applications using Open AI GPT models
Building images using Dall E model

Bonus Module 2: LangGraph & Crew AI

FAQ

A:The HAC program is a comprehensive, practical training course designed to help learners master Generative AI, Large Language Models (LLMs), and Agentic AI through real-world, hands-on projects using tools like LangChain, LLaMA, Agno, Groq, and Chromadb.

A: This course is ideal for students, developers, data scientists, AI enthusiasts, and professionals who want to gain in-depth practical skills in Generative AI, LLMs, and Agentic AI applications.

A: Basic programming knowledge (preferably in Python) is recommended, but the course is structured to guide learners from foundational AI concepts to advanced implementations step by step.

A: Unlike theoretical programs, HAC is hands-on and project-driven. Learners build real AI systems such as RAG pipelines, multi-agent architectures, and LangChain-based applications using modern frameworks and tools.

A: You’ll work with OpenAI APIs, Hugging Face, LangChain, LLaMA, Groq, Agno, and Chromadb, along with frameworks for transformers, vector databases, and prompt optimization.

A: By the end, participants will be able to design, develop, and deploy Generative AI models, AI agents, and multi-agent systems, and understand how to fine-tune, evaluate, and scale them ethically.

A:Learners will build prompt-based AI models, LangChain-powered chatbots, RAG pipelines, vector database search systems, and Agentic AI applications using LLaMA and Agno frameworks.

A: The course is divided into 12 modules, starting with AI fundamentals and progressing to advanced topics like transformers, prompt engineering, RAG systems, multi-agent systems, and ethics in GenAI.

A: Yes. Participants who complete all modules and projects successfully will receive a certificate of completion, validating their expertise in Generative AI and Agentic AI technologies.

A:Graduates will gain in-demand skills to pursue roles such as AI Engineer, Prompt Engineer, LLM Developer, Data Scientist, or AI Researcher, and will be equipped to contribute to real-world AI innovation and enterprise projects.