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Contents

1.1: Agentic AI and Your Career
1.2: Who Should Take this Course? Pre-requisites
1.3: Course Overview

2.1: AI Family Tree
2.2: Essential Concepts: Machine Learning
2.3: Essential Concepts: Deep Learning
2.4: Quiz

3.1: What is Generative AI?
3.2: Traditional AI vs Gen AI
3.3: What are AI Agents and Agentic AI?
3.4: Gen AI vs AI Agents vs Agentic AI
3.5: Real-world Applications for Gen AI & Agentic AI
3.6: Steps to Build Gen AI and Agentic Applications
3.7: Quiz
3.8: Exercise
3.9: Chapter Summary

4.1: What are Large Language Models?
4.2: How LLMs Work?
4.3: Context Window, Temperature, Top-p and Top-k
4.4: Challenges: Hallucinations, Security and Cost
4.5: What is a Vector Database?
4.6: What is RAG (Retrieval Augmented Generation)?
4.7: Quiz
4.8: Exercise
4.9: Chapter Summary

5.1: Elements of a Good Prompt
5.2: Zero-Shot, One-Shot, and Few-Shot Prompting
5.3: LangChain Installation
5.4: Groq and Ollama Setup
5.5: Calling LLM from Langchain
5.6: Prompt Templates & Chains
5.7: Output Parser
5.8: Build Financial Data Extraction App
5.9: Quiz
5.10: Exercise
5.11: Chapter Summary

6.1: Chromadb: Introduction and Installation
6.2: Basic Operations in Chromadb
6.3: Add, Update, Delete, Query
6.4: Metadata Filtering
6.5: Euclidean and Cosine Distance
6.6: Quiz
6.7: Exercise
6.8: Chapter Summary

7.1: Problem Statement
7.2: RAG Based Technical Architecture
7.3: Document Loaders
7.4: Text Splitters
7.5: Store Data in Vector Store (Chroma DB)
7.6: Retrieval and Answer Generation
7.7: Streamlit UI
7.8: Learning Lessons
7.9: Exercise

8.1: Problem Statement
8.2: SOW & Technical Architecture
8.3: Implement FAQ Handling
8.4: Routing using semantic-router
8.5: Streamlit UI: FAQ Handling
8.6: SQLite Database Setup
8.7: Implement Product Handling: SQL Query Generation
8.8: Implement Product Handling: Data Comprehension
8.9: Streamlit UI: Product Questions Handling
8.10: Bonus: Web Scraping
8.11: Exercise

9.1: Agency in AI
9.2: Build Your First Agent using Llama and Agno
9.3: Agent with Custom Tool
9.4: What are Reasoning Models?
9.5: Building a Reasoning Agent with Agno
9.6: Multimodal Agent
9.7: Other Frameworks: Smolagents, Google ADK
9.8: Quiz

10.1: What is Model Context Protocol (MCP)?
10.2: Build Your First MCP Server: Leave Management
10.3: Prebuilt MCP Servers
10.4: What is A2A Protocol?
10.5: Quiz
10.6: Exercise

11.1: Build Your First Multi Agent Program
11.2: When to consider Multi-Agent Systems?
11.3: Design Patterns for Multi-Agent Systems
11.4: Route Agent
11.5: Quiz

12.1: Introduction to Agentic AI Evaluation
12.2: Functional Evaluation
12.3: Hands on Functional Eval in Agno
12.4: Safety and Guardrails
12.5: Operational Metrics
12.6: Hands on Perf Eval in Agno
12.7: Quiz

13.1: Problem Statement & Tech Architecture
13.2: HR Management System (HRMS) APIs
13.3: Seed Data for HRMS
13.4: MCP Tools for Employee Management
13.5: Google App Password Setup for Emails
13.6: MCP Tools for Emails
13.7: MCP Prompt to Onboard a New Employee
13.8: MCP Tools for Tickets Management
13.9: Exercise

14.1: Introduction to Fine-Tuning
14.2: Low-Rank Adaptation (LoRA)
14.3: Quantization Basics
14.4: QLoRA
14.5: Fine-Tuning Llama with Unsloth
14.6: Quiz
14.7: Exercise

15.1: Ethical Challenges in Gen AI
15.2: Hallucination and Misinformation
15.3: Bias
15.4: PII and Privacy Laws
15.5: Environmental Impact
15.6: Copyrights and Intellectual Property
15.7: Quiz
15.8: Chapter Summary

16.1: Final Quiz

17.1: LangGraph Tutorial Bonus

18.1: Introduction to Transformer Architecture
18.2: Word Embeddings
18.3: Contextual Embeddings
18.4: Overview of Encoder and Decoder
18.5: Tokenization, Positional Embeddings
18.6: Attention Mechanism
18.7: Multi Headed Attention
18.8: Decoder
18.9: How Transformers are Trained?
18.10: Hugging Face: BERT Basics
18.11: Hugging Face: Spam Classification Using BERT
18.12: Hugging Face: Next Word Prediction Using GPT2
18.13: Exercise

Instructor

Dhaval Patel

Dhaval Patel

Data Entrepreneur | YouTuber | Ex - Bloomberg, Nvidia

I have 17 years of experience in Programming and Data Science working for big tech companies like NVIDIA and Bloomberg. I also run a famous YouTube channel called Codebasics where I pursue my passion for teaching. Codebasics is one of the top channels on YouTube when it comes to data science, machine learning, data structures, etc. I firmly believe that “Anyone can code” and I use analogies, simple explanations, and step-by-step storytelling to explain difficult concepts in such a way that even a high school student can understand them easily.

Operational Metrics

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