Become An Llm & Agentic Ai Engineer: 14-Day Bootcamp - 2025

Master Large Language Models, Hugging Face, AutoGen, CrewAI, LangChain, N8N, OpenAI Agents SDK, LangGraph, Gradio, & MCP

What you'll learn

Understand the foundations of Large Language Models (LLMs) and Agentic AI, including how LLMs are trained, fine-tuned, and deployed.

Create and deploy intelligent autonomous AI agents using cutting-edge frameworks like AutoGen, OpenAI Agents SDK, LangGraph, n8n, and MCP.

Explore and benchmark open-source LLMs such as LLama, DeepSeek, Qwen, Phi, and Gemma using Hugging Face and LM Studio.

Develop real-world applications using API access to OpenAI, Gemini, and Claude for text generation and vision tasks.

Apply a proven 5-step framework to select the right AI model for your business: maximizing cost-efficiency, minimizing latency, & accelerating time to market.

Evaluate LLMs using leaderboards like Vellum and Chat Arena, and conduct blind tests to objectively assess AI model performance.

Design Retrieval-Augmented Generation (RAG) pipelines using LangChain, OpenAI embeddings, & ChromaDB for efficient document retrieval & question answering.

Build an interactive, transparent AI-powered Q&A system with a Gradio interface that displays answers along with source citations for enhanced user trust.

Master data validation & structured output generation using the Pydantic library, including BaseModel, type hints, & parsed output creation from OpenAI models.

Build an AI-powered resume editor that analyzes gaps between a resume & job description & automatically tailors resumes/cover letters for targeted applications.

Learn how to fine-tune pre-trained open-source LLMs using parameter-efficient methods like LoRA and tools such as Hugging Face’s TRL and SFTTrainer.

Master dataset preparation and model evaluation techniques, including calculating accuracy, precision, recall, and F1-score using scikit-learn.

Apply key components in Hugging Face Transformers library such as pipeline( ), AutoTokenizer( ), and AutoModelForCausalLM( ).

Gain practical experience working with open-source datasets/models on Hugging Face, & apply quantization techniques like bitsandbytes to optimize Performance.

Master advanced prompt engineering techniques such as zero-shot, few-shot, and chain-of-thought prompting.

Deploy multi-model AI agents using AutoGen, integrating LLMs from OpenAI, Gemini, & Claude, enabling agent collaboration & human-in-the-loop oversight.

Develop and deploy agentic AI workflows using LangGraph, mastering concepts like states, edges, conditional logic, and multi-stage nodes.

Design & build AI-powered booking agents using LangGraph, enabling automated search & recommendation of flights & hotels through integration with external APIs.

Build a data science agent team using CrewAI, creating specialized agents for workflow planning, data analysis, model building, and predictive analytics.

Design and automate end-to-end Agentic AI workflows using n8n, integrating services like Gmail, Google Sheets, Google Calendar, and OpenAI.

Build an advanced AI tutor system using Model-Context-Protocol (MCP) and OpenAI Agents SDK, enabling dynamic tool interoperability.

Requirements

You will need a laptop and an internet connection!

No programming experience required; basic Python skills are a plus.

Description

The AI revolution is accelerating at an unimaginable pace, and those who master Large Language Models (LLMs) and Agentic AI will define the future of technology. The "Become an LLM & Agentic AI Engineer Bootcamp" is an intensive, 14-day hands-on program designed to equip professionals and enthusiasts with the skills needed to build real-world AI applications. Whether you’re a developer, data scientist, researcher, or technology leader, this bootcamp provides the tools and knowledge to navigate and innovate in this fast-evolving space confidently.You will begin by exploring the foundations of LLMs and agent frameworks, including how to benchmark models using LM Studio. The course then guides you through working with powerful closed-source APIs from providers like OpenAI, Gemini, and Claude. You will learn how to structure system and user messages, understand tokenization, and control outputs to build projects such as AI-powered text generators and vision-enabled calorie trackers.As you advance, you’ll dive into the world of open-source LLMs. You will fine-tune models on Hugging Face using state-of-the-art techniques like LoRA and Parameter-Efficient Fine-Tuning (PEFT). Alongside this, you’ll gain experience designing AI-powered web applications using Gradio, creating interactive streaming apps, and building intelligent AI tutors.A core component of the bootcamp focuses on mastering prompt engineering, including zero-shot, few-shot, and chain-of-thought prompting techniques to achieve consistent and controlled outputs. You'll also explore advanced capabilities such as building Retrieval-Augmented Generation (RAG) pipelines and working with embeddings for semantic search and knowledge retrieval.The program concludes with the development of next-generation AI agents. You will use frameworks like AutoGen, OpenAI Agents SDK, LangGraph, n8n, and MCP to create autonomous agents capable of interacting with external systems, APIs, and other digital tools. Each module emphasizes building practical, working projects that reinforce the learning objectives and prepare you for real-world deployment.This bootcamp is led by Dr. Ryan Ahmed, a highly experienced AI professor and educator who has taught over half a million learners globally. It is ideal for software engineers, data scientists, AI researchers, and technology professionals who want to break into the LLM and AI agent development space.The format of the program emphasizes project-based learning with step-by-step guidance, community interaction, and access to mentorship and continuous feedback. From Day 1, you’ll be building real-world applications, positioning yourself at the forefront of this transformative field.Enroll today, and I look forward to seeing you inside!

Overview

Section 1: Welcome to the Bootcamp!

Lecture 1 Instructor Introduction and LLM in Action!

Lecture 2 Download the Bootcamp Materials

Lecture 3 Bootcamp Outline

Lecture 4 Key Success Tips

Section 2: –––-PART A: CLOSED-SOURCE LLMs, GRADIO, & BENCHMARKING–––-

Lecture 5 Welcome to Part A of the Bootcamp!

Section 3: Day 1: Develop a Character AI Chatbot Using OpenAI API

Lecture 6 Task 1. Character AI Chatbot Project Introduction & Key Learning Objectives

Lecture 7 Task 2. Download Anaconda and Configure OpenAI API

Lecture 8 Task 3. Our First Chat with OpenAI API

Lecture 9 ❓Practice Opportunity Question: Test OpenAI API for Text Generation

Lecture 10 Practice Opportunity Solution: Test OpenAI API for Text Generation

Lecture 11 Task 4. Understand OpenAI API response Structure & Token Usage

Lecture 12 ❓Practice Opportunity Question: OpenAI Tokenizer Tool

Lecture 13 Practice Opportunity Solution: OpenAI Tokenizer Tool

Lecture 14 Task 5. Giving Our AI Chatbot a Personality Using the System Message!

Lecture 15 ❓Practice Opportunity Question: Changing AI Personalities

Lecture 16 Practice Opportunity Solution: Changing AI Personalities

Lecture 17 Conclusion, Summary, and Thank You!

Section 4: Day 2: Build an AI Calorie Tracker Using OpenAI API (Vision GPTs)

Lecture 18 Task 1. AI Calorie Tracker Project Introduction & Key Learning Objectives

Lecture 19 Task 2. Read a Sample Image Using Python's Pillow (PIL) Library

Lecture 20 ❓Practice Opportunity Question: Read & View Images Using PIL

Lecture 21 Practice Opportunity Solution: Read & View Images Using PIL

Lecture 22 Task 3. Understand Prompt Engineering Fundamentals

Lecture 23 ❓Practice Opportunity Question: Prompt Engineering Fundamentals

Lecture 24 Practice Opportunity Solution: Prompt Engineering Fundamentals

Lecture 25 Task 4. Perform Image Recognition Using OpenAI API's Vision GPT Models (Part A)

Lecture 26 Task 4. Perform Image Recognition Using OpenAI API's Vision GPT Models (Part B)

Lecture 27 ❓Practice Opportunity Question: Calling OpenAI API's Vision GPT Models

Lecture 28 Practice Opportunity Solution: Calling OpenAI API's Vision GPT Models

Lecture 29 Task 5. Obtain the Calorie Count of Food Images Using Vision GPT Models

Lecture 30 ❓Practice Opportunity Question: Expand API Payload to include Nutritional Value

Lecture 31 Practice Opportunity Solution: Expand API Payload to include Nutritional Value

Lecture 32 Conclusion, Summary, & Thank You Message!

Section 5: Day 3: Build an Adaptive LLM/AI Tutor with Gradio for Multi-level Learning

Lecture 33 Task 1. Introduction & Key Learning Objectives - Adaptive AI Tutor with Gradio

Lecture 34 Task 2. Learn Gradio 101 & Showcase Capabilities (Maps, Images, & Streaming)

Lecture 35 Task 3. Build and Test an AI Tutor Function (Without Gradio)

Lecture 36 ❓Practice Opportunity Question: Test AI Tutor Function with Many Personalities

Lecture 37 Practice Opportunity Solution: Test AI Tutor Function with Many Personalities

Lecture 38 Task 4. Build an Interactive Interface Using Gradio (No Streaming)

Lecture 39 ❓Practice Opportunity Question: Configure Gradio Interface Components

Lecture 40 Practice Opportunity Solution: Configure Gradio Interface Components

Lecture 41 Task 5. Add Streaming for an Enhanced Chat Experience in Gradio

Lecture 42 ❓Practice Opportunity Question: Streaming for an Enhanced Chat Experience

Lecture 43 Practice Opportunity Solution: Streaming for an Enhanced Chat Experience

Lecture 44 Task 6. Build a Multi-Level AI Tutor in Gradio with Explanation Level Slider

Lecture 45 ❓Practice Opportunity Question: Testing AI Tutor Slider Levels & Einstein Mode!

Lecture 46 Practice Opportunity Solution: Testing AI Tutor Slider Levels & Einstein Mode!

Lecture 47 Conclusion, Summary, & Thank You Message!

Section 6: Day 4: Build Websites with Claude, Gemini, & OpenAI & LLMs Leaderboards

Lecture 48 Task 1. Introduction & Module Objectives - Build Websites & LLMs Leaderboards

Lecture 49 Task 2. LLM Comparison, Benchmarks, & Vellum Leaderboard

Lecture 50 ❓Practice Opportunity Question: Vellum Leaderboard & LLMs Benchmarking

Lecture 51 Practice Opportunity Solution: Vellum Leaderboard & LLMs Benchmarking

Lecture 52 Task 3. Exploring Chatbot Arena and Blind AI/LLMs Models Testing

Lecture 53 ❓Practice Opportunity Question: Blind AI Testing Using Chatbot Arena

Lecture 54 Practice Opportunity Solution: Blind AI Testing Using Chatbot Arena

Lecture 55 Task 4. Setup API Key & Compare Math & Creative abilities of Claude, Gemini, GPT

Lecture 56 ❓Practice Opportunity Question: Compare LLMs Coding Abilities

Lecture 57 Practice Opportunity Solution: Compare LLMs Coding Abilities

Lecture 58 Task 5. Define the Startup Idea & Structure the Prompt

Lecture 59 ❓Practice Opportunity Question: Prompt Structuring for HTML Generation

Lecture 60 Practice Opportunity Solution: Prompt Structuring for HTML Generation

Lecture 61 Task 6. Generate Websites & HTML Landing Pages with OpenAI API

Lecture 62 ❓Practice Opportunity Question: HTML Landing Pages Generation

Lecture 63 Practice Opportunity Solution: HTML Landing Pages Generation

Lecture 64 Task 7. Generate HTML Landing Pages with Google Gemini-2.0-Flash API

Lecture 65 ❓Practice Opportunity Question: Compare Gemini Vs. OpenAI Website Generation

Lecture 66 Practice Opportunity Solution: Compare Gemini Vs. OpenAI Website Generation

Lecture 67 Task 8. Generate HTML Landing Pages with Anthropic Claude 3.7 Sonnet

Lecture 68 ❓Practice Opportunity Question: Website Design with LLM (Claude by Anthropic)

Lecture 69 Practice Opportunity Solution: Website Design with LLM (Claude by Anthropic)

Lecture 70 Conclusion, Summary, & Thank You Message!

Section 7: –––-PART B: OPEN-SOURCE LLMs, HUGGING FACE, RAG & FINE-TUNING–––-

Lecture 71 Welcome to Part B of this Bootcamp!

Section 8: Day 5: Hugging Face Open-Source Models

Lecture 72 Task 1. Project Overview: Chat with Documents Using Open-Source LLMs

Lecture 73 Task 2. Explore Hugging Face Models, Datasets, and Spaces

Lecture 74 ❓Practice Opportunity Question: Explore Hugging Face

Lecture 75 Practice Opportunity Solution: Explore Hugging Face

Lecture 76 Task 3. Install Key Libraries & Setup Access Tokens for Hugging Face

Lecture 77 ❓Practice Opportunity Question: GPU Access Check on Google Colab

Lecture 78 Practice Opportunity Solution: GPU Access Check on Google Colab

Lecture 79 Task 4. Hugging Face Transformers Library: Pipelines

Lecture 80 ❓Practice Opportunity Question: Transformers Pipelines

Lecture 81 Practice Opportunity Solution: Transformers Pipelines

Lecture 82 Task 5. Hugging Face Transformers Library: AutoTokenizers

Lecture 83 ❓Practice Opportunity Question: Transformers Library AutoTokenizer

Lecture 84 Practice Opportunity Solution: Transformers Library AutoTokenizer

Lecture 85 Task 6. Hugging Face Transformers Library: AutoModelForCasualLM

Lecture 86 ❓Practice Opportunity Question: Transformers AutoModelForCasualLM

Lecture 87 Practice Opportunity Solution: Transformers AutoModelForCasualLM

Lecture 88 Task 7. Read PDF Documents & Extract Content Using PyPDF Library

Lecture 89 ❓Practice Opportunity Question: PyPDF Library

Lecture 90 Practice Opportunity Solution: PyPDF Library

Lecture 91 Task 8. Build the Q&A Logic & Prompt the LLM (Microsoft Phi-4-mini)

Lecture 92 ❓Practice Opportunity Question: Test the Q&A Pipeline with Open-Source LLM

Lecture 93 Practice Opportunity Solution: Test the Q&A Pipeline with Open-Source LLM

Lecture 94 Task 9. Switch LLMs (LLama, Phi, & Gemma) & Build Gradio Interface

Lecture 95 ❓Practice Opportunity Question: Testing Qwen Open-Source LLM

Lecture 96 Practice Opportunity Solution: Testing Qwen Open-Source LLM

Lecture 97 Conclusion & Thank You!

Section 9: Day 6: Reasoning Open-Source LLMs on Hugging Face & Model Leaderboards

Lecture 98 Task 1. Introduction and Module Objectives - Reasoning LLMs on Hugging Face

Lecture 99 Task 2. Explore Hugging Face Datasets Library & Install Key Libraries

Lecture 100 ❓Practice Opportunity Question: Explore Hugging Face Datasets

Lecture 101 Practice Opportunity Solution: Explore Hugging Face Datasets

Lecture 102 Task 3. Load Financial News Datasets from Hugging Face

Lecture 103 ❓Practice Opportunity Question: Explore Financial News Datasets

Lecture 104 Practice Opportunity Solution: Explore Financial News Datasets

Lecture 105 Task 4. Load and Test DeepSeek Reasoning Model - Part 1

Lecture 106 Task 4. Load and Test DeepSeek Reasoning Model - Part 2

Lecture 107 ❓Practice Opportunity Question: Test Math Capabilities of DeepSeek

Lecture 108 Practice Opportunity Solution: Test Math Capabilities of DeepSeek

Lecture 109 Task 5. A Framework for Choosing the right AI Model for Your Business - Part 1

Lecture 110 Task 5. A Framework for Choosing the right AI Model for Your Business - Part 2

Lecture 111 Task 6. Model Leaderboards and Old/New Model Benchmarks - Part 1

Lecture 112 Task 6. Model Leaderboards and Old/New Model Benchmarks - Part 2

Lecture 113 Task 7. Prompting DeepSeek for Reasoning and Classification

Lecture 114 ❓Practice Opportunity Question: Analyze News Sentiment with DeepSeek

Lecture 115 Practice Opportunity Solution: Analyze News Sentiment with DeepSeek

Lecture 116 Task 8. Building Gradio Interface

Lecture 117 Conclusion and Thank You!

Section 10: Day 7: Build Retrieval Augmented Generation (RAG) Pipelines in LangChain

Lecture 118 Task 1. Introduction & Module Objectives - Build RAG Pipelines in LangChain

Lecture 119 Task 2. Understand Retrieval Augmented Generation (RAG) & Why Use it

Lecture 120 Task 3. LangChain 101 & Key Features

Lecture 121 Task 4. Setup, Gather RAG Tools & Load Datasets

Lecture 122 ❓Practice Opportunity Question: LangChain Textloader Testing

Lecture 123 Practice Opportunity Solution: LangChain Textloader Testing

Lecture 124 Task 5. Splitting (Chunking) Documents Using LangChain Text Splitter

Lecture 125 ❓Practice Opportunity Question: Configuring RecursiveCharacterTextSplitter

Lecture 126 Practice Opportunity Solution: Configuring RecursiveCharacterTextSplitter

Lecture 127 Task 6. Embeddings and Vector Store Creation

Lecture 128 ❓Practice Opportunity Question: Tensorflow Embeddings Projector

Lecture 129 Practice Opportunity Solution: Tensorflow Embeddings Projector

Lecture 130 Task 7. Testing the Retrieval Pipeline

Lecture 131 ❓Practice Opportunity Question: Retrieval Pipeline Testing

Lecture 132 Practice Opportunity Solution: Retrieval Pipeline Testing

Lecture 133 Task 8. Building and Testing RAG Pipeline in LangChain

Lecture 134 ❓Practice Opportunity Question: RetrievalQAWithSourcesChain Parameters

Lecture 135 Practice Opportunity Solution: RetrievalQAWithSourcesChain Parameters

Lecture 136 Task 9. Creating Gradio Interface for Our RAG Pipeline

Lecture 137 ❓Practice Opportunity Question: Gradio Interface Configuration & Testing

Lecture 138 Practice Opportunity Solution: Gradio Interface Configuration & Testing

Lecture 139 Conclusion, Summary, & Thank You Message!

Section 11: Day 8: Build a Resume & Cover Letter AI Assistant with OpenAI & Pydantic

Lecture 140 Task 1. Introduction to Resume & Cover Letter Building Project with Pydantic

Lecture 141 Task 2. Pydantic 101 & Python Type Hints

Lecture 142 ❓Practice Opportunity Question: Pydantic Models

Lecture 143 Practice Opportunity Solution: Pydantic Models

Lecture 144 Task 3. Generate Parsed Structured Output from OpenAI API with Pydantic

Lecture 145 ❓Practice Opportunity Question: Structured Output with OpenAI API & Pydantic

Lecture 146 Practice Opportunity Solution: Structured Output with OpenAI API & Pydantic

Lecture 147 Task 4. Define LLM Inputs Including Job Description & Original Resume

Lecture 148 ❓Practice Opportunity Question: Modify Job Description

Lecture 149 Practice Opportunity Solution: Modify Job Description

Lecture 150 Task 5. Enhance Resume with OpenAI's GPT-4.o

Lecture 151 ❓Practice Opportunity Question: Gemini API Testing

Lecture 152 Practice Opportunity Solution: Gemini API Testing

Lecture 153 Task 6. Perform Resume & Job Description Gap Analysis with LLMs

Lecture 154 ❓Practice Opportunity Question: Modify Functions to Include AI Skills

Lecture 155 Practice Opportunity Solution: Modify Functions to Include AI Skills

Lecture 156 Task 7. Generate a New Tailored Resume by AI with Change Tracking (Pydantic)

Lecture 157 ❓Practice Opportunity Question: Generate Resume Function Testing

Lecture 158 Practice Opportunity Solution: Generate Resume Function Testing

Lecture 159 Task 8. Generate a Custom Cover Letter

Lecture 160 ❓Practice Opportunity Question: Generate Cover Letter Function Testing

Lecture 161 Practice Opportunity Solution: Generate Cover Letter Function Testing

Lecture 162 Task 9. Unified Resume and Cover Letter Generation Function

Lecture 163 ❓Practice Opportunity Question: Test the Entire Workflow with New Data

Lecture 164 Practice Opportunity Solution: Test the Entire Workflow with New Data

Lecture 165 Concluding Remarks and Thank You!

Section 12: Day 9: Fine-Tuning of Large Language Models with LORA, SFTTrainer, PEFT, & TRL

Lecture 166 Task 1. Project Introduction and Welcome Message: Fine-Tuning of LLMs

Lecture 167 Task 2. Import Key Libraries and Datasets

Lecture 168 ❓Practice Opportunity Question: GPU Detection Tesla T4 & A100

Lecture 169 Practice Opportunity Solution: GPU Detection Tesla T4 & A100

Lecture 170 Task 3. Load and Prepare the Financial News Datasets

Lecture 171 ❓Practice Opportunity Question: Explore Data Imbalance & Seaborn Countplot

Lecture 172 Practice Opportunity Solution: Explore Data Imbalance & Seaborn Countplot

Lecture 173 Task 4. Format the Data into Supervised Fine-Tuning (SFT) Trainer Format

Lecture 174 ❓Practice Opportunity Question: Formatting DeepSeek Models in SFTTrainer Format

Lecture 175 Practice Opportunity Solution: Formatting DeepSeek Models in SFTTrainer Format

Lecture 176 Task 5. Understand Confusion Matrix & Classification KPIs (Precision, recall,..)

Lecture 177 Task 6. Perform Zero-Shot Classification With Base Model (Inference) - Part 1

Lecture 178 Task 6. Perform Zero-Shot Classification With Base Model (Inference) - Part 2

Lecture 179 ❓Practice Opportunity Question: Zero-Shot Inference on Base Model

Lecture 180 Practice Opportunity Solution: Zero-Shot Inference on Base Model

Lecture 181 Task 7. Perform LLMs fine-tuning Using PEFT, LORA, & SFTTrainer

Lecture 182 Task 8. Evaluate Fine-Tuned Large Language Models

Lecture 183 ❓Practice Opportunity Question: Plot Confusion Matrix & KPIs for Fine-Tuned LLM

Lecture 184 Practice Opportunity Solution: Plot Confusion Matrix & KPIs for Fine-Tuned LLM

Lecture 185 Conclusion, Summary, & Thank You!

Section 13: –––-PART C: AI AGENTS WITH LANGGRAPH, AUTOGEN, CREWAI, N8N, & MCP–––-

Lecture 186 Welcome to Part C of this bootcamp!

Section 14: Day 10: Build Multi-Model AI Agent Teams Using AutoGen

Lecture 187 Task 1. Introduction & Module Objectives - Build AI Agents Teams with AutoGen

Lecture 188 Task 2. Understand AutoGen Capabilities & Key Features

Lecture 189 ❓Practice Opportunity Question: AI Agents Teams Design

Lecture 190 Practice Opportunity Solution: AI Agents Teams Design

Lecture 191 Task 3. Create Our First AI Agents in AutoGen with OpenAI GPT-4o

Lecture 192 ❓Practice Opportunity Question: Building AI Agents in AutoGen

Lecture 193 Practice Opportunity Solution: Building AI Agents in AutoGen

Lecture 194 Task 4. Test AI Agents Conversations with Similar LLM (OpenAI GPT-4o)

Lecture 195 ❓Practice Opportunity Question: Modify initiate_chat() Function Parameters

Lecture 196 Practice Opportunity Solution: Modify initiate_chat() Function Parameters

Lecture 197 Task 5. Configure Multi-Model AI Agents in AutoGen with Gemini & OpenAI's GPT-4o

Lecture 198 ❓Practice Opportunity Question: Configure AI Agents using Anthropic's Claude

Lecture 199 Practice Opportunity Solution: Configure AI Agents using Anthropic's Claude

Lecture 200 Task 6. Trigger Multi-Model AI Agents Conversations in AutoGen

Lecture 201 ❓Practice Opportunity Question: Adjusting AI Agent's Creativity Level

Lecture 202 Practice Opportunity Solution: Adjusting Agent's Creativity Level

Lecture 203 Task 7. Adding Human (User Proxy Agent) & Leveraging Group Chat

Lecture 204 ❓Practice Opportunity Question: Adding Claude's Social Media Strategist to Chat

Lecture 205 Practice Opportunity Solution: Adding Claude's Social Media Strategist to Chat

Lecture 206 Conclusion, Summary, & Thank You Message!

Section 15: Day 11: Building AI Agentic Workflows in LangGraph

Lecture 207 Task 1. Project Introduction - Building Agentic Workflows in LangGraph

Lecture 208 Task 2. Understand LangGraph Components (Nodes, Edges, & State Graph) & Features

Lecture 209 Task 3. Build Your First Agentic AI Workflow in LangGraph - Part 1

Lecture 210 Task 3. Build Your First Agentic AI Workflow in LangGraph - Part 2

Lecture 211 ❓Practice Opportunity Question: Test Summarization AI Agent in LangGraph

Lecture 212 Practice Opportunity Solution: Test Summarization AI Agent in LangGraph

Lecture 213 Task 4. Build Multi Node Agentic Workflow in LangGraph

Lecture 214 ❓Practice Opportunity Question: Add a New Node (Sentiment) to Agentic Workflow

Lecture 215 Practice Opportunity Solution: Add a New Node (Sentiment) to Agentic Workflow

Lecture 216 Task 5. Develop Agentic AI Workflow with One Tool & Conditional Edges - Part 1

Lecture 217 Task 5. Develop Agentic AI Workflow with One Tool & Conditional Edges - Part 2

Lecture 218 ❓Practice Opportunity Question: Calling Tools in LangGraph

Lecture 219 Practice Opportunity Solution: Calling Tools in LangGraph

Lecture 220 Task 6. Create and Add a New Custom Tool to LangGraph Workflows

Lecture 221 ❓Practice Opportunity Question: Define New Custom Tools in LangGraph

Lecture 222 Practice Opportunity Solution: Define New Custom Tools in LangGraph

Lecture 223 Task 7. Leverage LangGraph to Perform Flight Search with Amadeus Tool & ToolNode

Lecture 224 ❓Practice Opportunity Question: Adding Hotel Search Tool Using Amadeus

Lecture 225 Practice Opportunity Solution: Adding Hotel Search Tool Using Amadeus

Lecture 226 Task 8. Bringing Everything Together & Building the AI Booking Agent

Lecture 227 ❓Practice Opportunity Question: Test the AI Agent Booking Tool

Lecture 228 Practice Opportunity Solution: Test the AI Agent Booking Tool

Lecture 229 Task 9. Build a Gradio Integration for the Booking AI Agent in LangGraph

Lecture 230 Summary & Thank You!

Section 16: Day 12: Build A Team of Data Science AI Agents Using CrewAI

Lecture 231 Task 1. Project Intro - Build a Team of Data Scientists Using CrewAI

Lecture 232 Task 2. Build Train and Evaluate ML Models Regression Overview

Lecture 233 Task 2A. Project Introduction ML regression

Lecture 234 Task 2B. Machine Learning Regression 101

Lecture 235 ❓Practice Opportunity Question: Regression 101

Lecture 236 Practice Opportunity Solution: Regression 101

Lecture 237 Task 2C. Import Libraries & Perform Data Inspection - Part 1

Lecture 238 Task 2C. Import Libraries & Perform Data Inspection - Part 2

Lecture 239 ❓Practice Opportunity Question: Perform Data Inspection

Lecture 240 Practice Opportunity Solution: Perform Data Inspection

Lecture 241 Task 2D. Data Imputation & Handling Missing Dataset

Lecture 242 ❓Practice Opportunity Question: Data Imputation & Handling Missing Dataset

Lecture 243 Practice Opportunity Solution: Data Imputation & Handling Missing Dataset

Lecture 244 Task 2E. Perform Exploratory Data Analysis (EDA) and Visualization

Lecture 245 ❓Practice Opportunity Question: EDA and Visualization

Lecture 246 Practice Opportunity Solution: EDA and Visualization

Lecture 247 Task 2F. Data Pre-Processing (One-Hot-Encoding & Train/Test Split)

Lecture 248 ❓Practice Opportunity Question: Data Pre-Processing & Train/Test Split

Lecture 249 Practice Opportunity Solution: Data Pre-Processing & Train/Test Split

Lecture 250 Task 2G. Build Linear Regression Models Using Scikit-Learn Library

Lecture 251 ❓Practice Opportunity Question: Build Linear Regression Models

Lecture 252 Practice Opportunity Solution: Build Linear Regression Models

Lecture 253 Task 2H. Build Random Forest Regression Models Using Scikit-Learn

Lecture 254 ❓Practice Opportunity Question: Build Random Forest & XG-Boost Regression Models

Lecture 255 Practice Opportunity Solution: Build Random Forest & XG-Boost Regression Models

Lecture 256 Task 2I. Feature Importance Analysis

Lecture 257 ❓Practice Opportunity Question: Feature Importance Analysis

Lecture 258 Practice Opportunity Solution: Feature Importance Analysis

Lecture 259 Task 3. Understand CrewAI Key Components (Agents, Tasks, Tools)

Lecture 260 Task 4. Import and Test NotebookCodeExecutor Tool

Lecture 261 ❓Practice Opportunity Question: Test NotebookCodeExecutor Tool

Lecture 262 Practice Opportunity Solution: Test NotebookCodeExecutor Tool

Lecture 263 Task 5. Define Multiple AI Agents in CrewAI

Lecture 264 ❓Practice Opportunity Question: Modify Existing AI Agents

Lecture 265 Practice Opportunity Solution: Modify Existing AI Agents

Lecture 266 Task 6. Define Key Tasks in CrewAI & Responsible Agents

Lecture 267 Task 7. Creating & Assembling the Crew & Automating Data Science Workflow!

Lecture 268 ❓Practice Opportunity Question: Modifying Tasks to Build Decision Trees

Lecture 269 Practice Opportunity Solution: Modifying Tasks to Build Decision Trees

Lecture 270 Summary & Concluding Remarks

Section 17: Day 13: Build Agentic AI Workflows in n8n

Lecture 271 Introduction to n8n, Key Features, and Module Learning Objectives

Lecture 272 Build Your First Summarization Agentic AI Workflow in n8n

Lecture 273 Export Workflow, Track Variables and Monitor Logs

Lecture 274 ❓Practice Opportunity Question: Build a Translation Agentic Workflow Using Claud

Lecture 275 Practice Opportunity Solution: Build a Translation Agentic Workflow Using Claude

Lecture 276 Adding Search Capabilities, Memory, and Exploring n8n Templates

Lecture 277 ❓Practice Opportunity Question: Test the Agent Search Capabilities

Lecture 278 Practice Opportunity Solution: Test the Agent Search Capabilities

Lecture 279 Adding Google Sheet Integrations in n8n Agentic Workflows

Lecture 280 ❓Practice Opportunity Question: Build Agentic AI Workflow to Convert from Python

Lecture 281 Practice Opportunity Solution: Build Agentic AI Workflow to Convert from Python

Lecture 282 Generate Parsed Structured Output Using Output Parser in n8n

Lecture 283 Build Workflows to Schedule Calendar Meetings in Google Calendars

Lecture 284 Adding Email Triggering Capability

Section 18: Day 14: Build AI Agents with Model Context Protocol (MCP) & OpenAI Agents SDK

Lecture 285 Task 1. Project Overview with MCP & OpenAI Agents SDK

Lecture 286 Task 2. Understanding Model Context Protocol (MCP)

Lecture 287 Task 3. Install Key Libraries and Configure APIs

Lecture 288 Task 4A. Build and Configure the MCP Server with Tools (Part 1)

Lecture 289 Task 4A. Build and Configure the MCP Server with Tools (Part 2)

Lecture 290 Task 4B. Launch the MCP Server

Lecture 291 ❓Practice Opportunity Question: Adding a New Tool to the MCP Server

Lecture 292 Practice Opportunity Solution: Adding a New Tool to the MCP Server

Lecture 293 Task 5. Explore Tools on MCP Server and Fetch the Manifest (Schema)

Lecture 294 ❓Practice Opportunity Question: MCP Server Manifest (Schema)

Lecture 295 Practice Opportunity Solution: MCP Server Manifest (Schema)

Lecture 296 Task 6. Create an AI Agent Using OpenAI Agents SDK With MCP Tools

Lecture 297 Conclusion, Summary, & Thank You!

Section 19: Congratulations and Thank You!

Lecture 298 Congratulations and Thank You!

Data scientists, ML engineers, and AI researchers who want to move into the agentic AI and LLM application space.,Software developers with basic Python skills who want to integrate cutting-edge LLMs and agent frameworks into real-world applications.,Tech professionals and AI enthusiasts interested in exploring open-source models (like LLaMA, DeepSeek, Owen, Phi) and frameworks (AutoGen, LangGraph, CrewAI, n8n).,Corporate innovation teams or R&D teams wanting to prototype AI-powered workflows, assistants, and automations.,Advanced students and educators looking for practical, hands-on experience with LLMs, fine-tuning, and prompt engineering.,Entrepreneurs and startups exploring AI-powered products like autonomous agents, resume editors, booking agents, and data science assistants.