Ai & Llm Engineering Mastery: Genai, Rag Complete Guide

From Fundamentals to Advanced AI Engineering – Fine-Tuning, RAG, AI Agents, Vector Databases & Real-World Projects

What you'll learn

Master the architecture and workflow of a RAG system for processing PDFs and multimodal data.

Master the Fundamentals of AI, Machine Learning and Deep Learning (Basics)

Master LangChain tools, frameworks, and workflows, including embedding techniques and retrievers.

Fine-tuning models with OpenAI, LoRA, and other techniques to customize AI responses.

Develop AI-driven applications with advanced RAG techniques, multimodal search, and AI agents for real-world use cases.

Requirements

Basics of Programming - Python Fundamentals INCLUDED

Description

Become an AI Engineer and master Large Language Models (LLMs), Generative AI, Retrieval-Augmented Generation (RAG), AI agents, and vector databases in this comprehensive hands-on course. Whether a beginner or an experienced developer, this course will take you from zero to hero in building real-world AI-powered applications.This course combines deep theoretical insights with hands-on projects, ensuring you understand AI model architectures, development and optimization strategies, and practical applications.What You’ll Learn:Deep Learning & Machine Learning FoundationsUnderstand neural networks, activation functions, transformers, and the evolution of AI.Learn how modern AI models are trained, optimized, and deployed in real-world applications.Master Large Language Models (LLMs) & Transformer-Based AIDeep dive into OpenAI models, and open-source AI frameworks.Build and deploy custom LLM-powered applications from scratch.Retrieval-Augmented Generation (RAG) & AI-Powered SearchLearn how AI retrieves knowledge using vector embeddings, FAISS, and ChromaDB.Implement scalable RAG systems for AI-powered document search and retrieval.LangChain & AI Agent WorkflowsBuild AI agents that autonomously retrieve, process, and generate information.Fine-Tuning LLMs & Open-Source AI ModelsFine-tune OpenAI, and LoRA models for custom applications.Learn how to optimize LLMs for better accuracy, efficiency, and scalability.Vector Databases & AI-Driven Knowledge RetrievalWork with FAISS, ChromaDB, and vector-based AI search workflows.Develop AI systems that retrieve and process structured & unstructured data.Hands-on with AI Deployment & Real-World ApplicationsBuild AI-powered chatbots, multimodal RAG applications, and AI automation tools.Who Should Take This Course?Aspiring AI Engineers & Data Scientists – Looking to master LLMs, AI retrieval, and search systems.Developers & Software Engineers – Who want to integrate AI into their applications.Machine Learning Enthusiasts – Seeking a deep dive into AI, GenAI, and AI-powered search.Tech Entrepreneurs & Product Managers – Wanting to build AI-driven SaaS products.Students & AI Beginners – Who need a structured, step-by-step path from beginner to expert.Course RequirementsNo prior AI experience required – the course takes you from beginner to expert.Basic Python knowledge (recommended but not required - Python Fundamentals Included in the course).Familiarity with APIs & JSON is helpful but not mandatory.A computer with internet access for hands-on development.Why Take This Course?Comprehensive AI Training: Covers LLMs, RAG, AI Agents, Vector Databases, Fine-Tuning.Hands-On Projects: Every concept is reinforced with real-world AI applications.Up-to-Date & Practical: Learn cutting-edge AI techniques & tools used in top tech companies.Zero to Hero Approach: Designed for absolute beginners & experienced developers alike.Master AI Engineering and become an expert in GenAI, LLMs, and RAG today.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 DEMO - What You'll Build in this Course

Lecture 3 Course Structure

Lecture 4 How To Get The Most from This Course

Section 2: Development Environment Setup

Lecture 5 Development Environment Setup - Overview

Lecture 6 Install Python on Windows - for WINDOWS USERS

Lecture 7 Install Python on MAC - for MAC USERS

Lecture 8 Download Visual Studio Code

Lecture 9 Install the Python Extension Pack for VS Code

Lecture 10 Running First Python Program in VS Code

Section 3: Do You Know Python?

Lecture 11 Python Deep Dive - Introduction and Overview

Section 4: OPTIONAL - Python Deep Dive - Master Python Fundamentals

Lecture 12 What is Python and Where It's Used?

Lecture 13 Python Compilation & Interpretation Process

Lecture 14 Download Python Fundamentals Code

Lecture 15 Declaring Variables in Python

Lecture 16 Data Types

Lecture 17 Python f-Strings

Lecture 18 Numbers - Integers and Floats

Lecture 19 Introduction to Lists - Accessing and Modifying Them

Lecture 20 f-Strings & Individual Values from a List

Lecture 21 Sorting a List and Getting a List Length

Lecture 22 Lists and Loops - Looping through a List

Lecture 23 Making a List of Numbers with Loops and the Range Function

Lecture 24 Statistics Functions for Numbers

Lecture 25 Generate Even Numbers with the List and Range

Lecture 26 Important: Code Organization Note

Lecture 27 List Comprehension

Lecture 28 Tuples

Lecture 29 Branching - If Statements and Booleans

Lecture 30 The Elif and the in Keywords

Lecture 31 Hands-on - Using AND and OR Logical Operators

Lecture 32 AND OR Logical Operators

Lecture 33 Checking for Inequalities

Lecture 34 Hands-on - Inner If-Statements

Lecture 35 Data Structures - Dictionaries - Introduction and Declaring and Accessing Values

Lecture 36 Modifying a Dictionary

Lecture 37 Iterating Through a Dictionary

Lecture 38 Nested Dictionaries and Looping Through Them

Lecture 39 Looping through a Dictionary with a List Inside

Lecture 40 User Input and While Loops - User Input - Introduction

Lecture 41 Hands-on - Odd or Even Number

Lecture 42 While Loops & Simple Quit Program

Lecture 43 Hands-on - Quiz Game

Lecture 44 Removing all Instances of Specific Values from a List

Lecture 45 Hands-on Dream Travel Itinerary Program - Filling a Dictionary with User Input

Lecture 46 Functions - Introduction

Lecture 47 Passing Information to a Function (parameters)

Lecture 48 Positional and Named Arguments

Lecture 49 Default Values - Parameters

Lecture 50 Return Values from a Function

Lecture 51 Hands-on - Returning an Integer & Intro do DocString

Lecture 52 Functions - Passing a List as Argument

Lecture 53 Passing an Arbitrary Number of Arguments to a Function

Lecture 54 Introduction to Modules - Importing Specific functions from a Module

Lecture 55 Using the "as" as an Alias

Lecture 56 Classes and OOP - Object Oriented Programming - The "init and "str" methods

Lecture 57 Adding More Methods to the Class

Lecture 58 Setting a Default Value for an Attribute

Lecture 59 Modifying Class Attribute - directly and with Methods

Lecture 60 Inheritance - Create an Ebook - Child Class

Lecture 61 Overriding Methods

Lecture 62 Creating and Importing from a Module

Lecture 63 The Object Class - Overview

Lecture 64 The Python Standard Library

Lecture 65 Random Module - Random Fruit Hands-on

Lecture 66 Hands-on - Random Fruit with Choice Module Method

Lecture 67 Using Datetime Module

Lecture 68 Writing & Reading Files - Do Useful Tasks with Python - Do amazing things

Lecture 69 The Path Class & Reading a Text File

Lecture 70 Resolving Path - Reading From a Subdirectory with Path

Lecture 71 Path Properties Overview

Lecture 72 Writing to Text file with Path

Lecture 73 Read and Write to File Using the "with" Keyword

Lecture 74 Handling Exceptions

Lecture 75 The "FileNotFound" and "IndexError" Exceptions Types

Lecture 76 Custom Exception Creation and handling

Lecture 77 JSON - Reading and Writing to a JSON File

Lecture 78 Hands-on - Writing and Reading - Countries to JSON file

Lecture 79 Hands-on - File Organizer

Lecture 80 Python Virtual Environment and PIP

Lecture 81 Setting up Virtual Environment and Installing a Package

Lecture 82 Hands-on Watermarker Python Tool

Lecture 83 Building an Image Watermarker in Python - Part 1

Lecture 84 Generating the Watermarked Images

Lecture 85 Reading CSV File - Introduction

Lecture 86 Getting the CSV header Position

Lecture 87 Reading Data from a CSV Column

Lecture 88 Plotting a Graph with CSV Data

Section 5: Deep and Machine Learning Deep Dive

Lecture 89 Deep and Machine Learning Deep Dive - Overview and Breakdown

Lecture 90 Deep Learning Key Aspects

Lecture 91 Deep Neural Network Dissection - Full Dive with Analogies

Lecture 92 The Single Neuron Computation - Deep Dive

Lecture 93 Wights - Deep Dive

Lecture 94 Activation Functions - Deep Dive with Analogies

Lecture 95 Deep Learning Summary

Lecture 96 Machine Learning Introduction - Machine Learning vs. Deep Learning

Lecture 97 Learning Types - Education System Analogy

Lecture 98 Comparative Capabilities Deep Learning and Machine Learning and AI - Summary

Section 6: Generative AI (GenAI) - Deep Dive

Lecture 99 GenAI Introduction and Architecture Overview

Lecture 100 GenAI Key Technologies - Limitations and challenges

Lecture 101 GenAI Key Components Overview and Summary

Section 7: LLMs (Large Language Models) - Fundamentals - A Deep Dive

Lecture 102 LLMs - Overview

Lecture 103 The Transformer Architecture - Fundamentals

Lecture 104 The Self-Attention Mechanism - Analogy

Lecture 105 The Transformers Library - Deep Dive

Lecture 106 HANDS-ON - Create a Simple LLM from the Transformers Library - Simple

Lecture 107 HANDS-ON - Hands-on Enhanced Transformers LLM

Lecture 108 Open-source vs. Closed-source Models - Overview

Section 8: OpenAI Models and Setup

Lecture 109 Setup OpenAI Account and API Key

Lecture 110 Using APIs Effectively in AI Projects

Lecture 111 HANDS-ON - Making our First Call to OpenAI Model

Section 9: Prompt Engineering - Communicating with LLMs - Deep Dive

Lecture 112 Prompt Engineering Introduction

Lecture 113 Prompt Engineering and Types - Why it Matters

Lecture 114 HANDS-ON - Simple Prompting Example

Lecture 115 Advanced Prompting Techniques and Challenges

Lecture 116 HANDS-ON - Few-shots Prompting

Lecture 117 HANDS-ON - Zero-shot Prompting

Lecture 118 HANDS-ON -Chain-of-Thoughts Prompting

Lecture 119 HANDS-ON - Instructional Prompting

Lecture 120 HANDS-ON - Role-Playing and Open-ended Prompting

Lecture 121 Temperature and Top-p Sampling

Lecture 122 HANDS-ON - Prompt Techniques Combination and Streaming

Lecture 123 Prompt Engineering Summary and Takeaways

Section 10: Ollama & Open-Source Models - Complete Guide

Lecture 124 Ollama - Introduction

Lecture 125 Download Source Code and Resources

Lecture 126 Ollama Deep Dive - Ollama Overview - What is Ollama and Advantages

Lecture 127 Ollama Key Features and Use Cases

Lecture 128 System Requirements & Ollama Setup - Overview

Lecture 129 HANDS-ON - Download and Setup Ollama and Llama3.2 Model

Lecture 130 Ollama Models Page - Overview

Lecture 131 Ollama Model Parameters Deep Dive

Lecture 132 Understanding Parameters and Disk Size and Computational Resources Needed

Lecture 133 Ollama CLI Commands -Pull and Testing a Model

Lecture 134 Pull in the Llava Multimodal Model and Caption an Image

Lecture 135 Summarization and Sentiment Analysis & Customizing Our Model

Lecture 136 Ollama REST API - Generate and Chat Endpoints

Lecture 137 Ollama REST API - Request JSON Mode

Lecture 138 Ollama Models Support Different Tasks - Summary

Lecture 139 Different Ways to Interact with Ollama Models

Lecture 140 Ollama Model Running Under Msty App

Lecture 141 Ollama Python SDK for Building LLM Local Applications

Lecture 142 HANDS-ON - Interact with Llama3 in Python Using Ollama REST API

Lecture 143 Ollama Python Library - Chatting with a Model

Lecture 144 Chat Example with Streaming

Lecture 145 Using Ollama Show Function

Lecture 146 Create a Custom Model in Code

Section 11: Context & Memory Management for LLMs - Deep Dive

Lecture 147 HANDS-ON - Context and Memory Management Overview

Lecture 148 What is Context and Memory Management - Deep Dive

Lecture 149 HANDS-ON - Adding Memory and Context to Chatbox

Lecture 150 Summary

Section 12: Logging in LLM Applications - Deep Dive

Lecture 151 Logging - Introduction - What and the Why

Lecture 152 Logging in LLM Applications and Logging Life Cycle

Lecture 153 HANDS-ON - Chatbot with Logging

Lecture 154 Summary

Section 13: RAG - Retrieval-Augmented Generation - Deep Dive

Lecture 155 RAG Introduction - What is it?

Lecture 156 RAG Key Components - The RAG Triad

Lecture 157 RAG vs. Pure GenAI Models

Lecture 158 RAG Deep Dive - Full Diagram Walkthrough

Lecture 159 RAG Benefits and Practical Applications

Lecture 160 RAG Challenges

Lecture 161 RAG Fundamentals - Takeaways - Summary

Section 14: Vector Databases and Embeddings - Deep Dive

Lecture 162 Vector Databases and Embeddings for RAG Workflows - Introduction

Lecture 163 Download Source code

Lecture 164 Introduction to Vector Databases - Full Overview

Lecture 165 Why Vector Databases

Lecture 166 Vector Databases - Benefits and Advantages

Lecture 167 Traditional vs. Vector Databases - Limitations and challenges

Lecture 168 Vector Databases & Embeddings - Full Overview

Lecture 169 Embeddings vs. Vectors - Differences

Lecture 170 Vector Databases - How They Work and Advantages

Lecture 171 Vector Databases Use Cases

Lecture 172 Vector and Traditional Databases - Summary

Lecture 173 The Top 5 Vector Databases - Overview

Lecture 174 Building Vector Databases - Dev Environment Setup

Lecture 175 Setup VS-Code, Python and OpenAI API Key

Lecture 176 Chroma Database workflow

Lecture 177 Creating a ChromaDB and Adding Documents and Querying

Lecture 178 Looping Through the Results & Showing Similarity Search Results

Lecture 179 Chroma Default Embedding Function

Lecture 180 Chroma Vector Database - Persisting Data and Saving

Lecture 181 Creating an OpenAI Embeddings - Raw without Chroma

Lecture 182 Using OpenAIs Embedding API to Create Embedding in ChromaDB

Lecture 183 Vector Databases Metrics and Data Structures

Lecture 184 Summary

Lecture 185 Vector Similarity Deep Dive - Cosine Similarity

Lecture 186 Eucledian Distance - L2 Norm

Lecture 187 Dot Product

Lecture 188 Summary

Lecture 189 Vector Databases and LLM - Deep Dive

Lecture 190 Loading all Documents

Lecture 191 Generating Embeddings from Documents and Insert to Vector Database

Lecture 192 Getting the Relevant Chunks when Given a Query

Lecture 193 Using OpenAI LLM to Generate Response - Full Workflow

Lecture 194 Summary

Section 15: HANDS-ON - RAG PDF Workflow - Build RAG Workflows Deep Dive

Lecture 195 Building a RAG Pipeline - Overview

Lecture 196 First RAG Workflow Architectural Diagram

Lecture 197 Setting up the Embedding Model Class

Lecture 198 HANDS-ON - Building and Showcasing the RAG Workflow

Lecture 199 HANDS-ON - RAG Workflow with UI - Streamlit

Lecture 200 First RAG Pipeline Summary

Section 16: HANDS-ON - Build a PDF RAG System with Text Chunking

Lecture 201 PDF RAG Workflow - Architecture Overview

Lecture 202 PDF and Chunk Processing and Chunk Overlap - Deep Dive

Lecture 203 Setting up the SimpleRAGSystem Class and Methods

Lecture 204 Testing the PDF RAG System

Lecture 205 Simple PDF RAG Workflow - Summary

Section 17: LLM Tools and Frameworks - LangChain Deep Dive

Lecture 206 LLM Frameworks Introduction - LangChain Fundamentals

Lecture 207 What is LangChain and and Main Components

Lecture 208 LangChain Setup and ChatModel

Lecture 209 Hands-on - LangChain ChatPromptTemplates

Lecture 210 Indexes, Retrievers and Data Preparation - Overview

Lecture 211 Hands-On - LangChain TextLoaders

Lecture 212 Hands-on: Text Splitting and Cleaning

Lecture 213 Hands-on: Embeddings and Retriever with FAISS VectorStore

Lecture 214 LangChain TextSplitter - Deep Dive

Lecture 215 LangChain DirectoryLoader

Lecture 216 LangChain PDFLoader

Lecture 217 Hands-on: LangChain Chains

Lecture 218 Hands-on - Simple RAG System with Chat and LangChain Chains

Lecture 219 Hands-on: Full RAG System QA Bot Using LangChain

Section 18: HANDS-ON - Building LLM Applications with LangChain

Lecture 220 LLM Application - News Summarizer - Architectural Overview

Lecture 221 News Summarizer - Full Implementation

Lecture 222 LLM Application - Youtube Video Summarizer - Architectural Overview

Lecture 223 Youtube Video Summarizer & Q&A Dependency Setup

Lecture 224 Youtube Video Summarizer Class Setup and Walkthrough

Lecture 225 Youtube Video Summarizer Q&A - Testing the Workflow

Lecture 226 LLM Application - Voice Assistant RAG System - Architectural Overview

Lecture 227 Voice Assistant RAG System - Demo

Lecture 228 Voice Assistant RAG System - Walkthrough and Demo

Section 19: Advanced RAG Techniques - Naive vs Advanced RAG Techniques

Lecture 229 RAG and the RAG Triad - Quick Overview and Recap

Lecture 230 What is RAG and Naive RAG Overview and Pitfalls - Motivation

Lecture 231 Deep Dive into Each Naive RAG Drawbacks

Lecture 232 Advanced RAG Technique - Query Expansion with Multiple Queries - Overview

Lecture 233 Hands-on - Query Expansion with Multiple Queries - Generate Multiple Queries

Lecture 234 Query Expansion Workflow Architectural Diagram

Lecture 235 Hands-on- Setting up the Workflow and Code Walkthrough

Lecture 236 Query Expansion Full RAG Workflow

Lecture 237 Query Expansion with Multiple Queries Downsides & Summary

Lecture 238 Re-Ranking & Cross-encoder and Bi-encoders - Overview

Lecture 239 Reranking Technique RAG System Workflow Architecture

Lecture 240 Cohere Rerank API Key Setup

Lecture 241 Hands-on - Re-ranking Implementation with Cohere - Full Implementation

Lecture 242 Re-ranking Summary

Section 20: Multimodal RAG - Deep Dive

Lecture 243 Multimodal RAG Source Code

Lecture 244 RAG & Multimodal RAG - Recap and Overview

Lecture 245 RAG Benefits and Practical Applications

Lecture 246 Multimodal RAG - Overview & Motivation and Benefits - How it Works

Lecture 247 How Search Is Integrated into a Multimodal RAG System - Full Workflow

Lecture 248 Why Multimodal Search is so Powerful

Lecture 249 Visual Explanation Why Multimodal Search is so Powerful

Lecture 250 HANDS-on: Multimodal Search System setup - Create Embeddings from Images

Lecture 251 Finish the Multimodal Search System

Lecture 252 HANDS-ON - Multimodal Recommender System - Overview

Lecture 253 Getting our Dataset from HuggingFace & showing Number of Rows

Lecture 254 Saving Images Embeddings to Vector Database

Lecture 255 Testing our MultiModal Recommender System - Fetching the Correct Images

Lecture 256 Setting up the RAG Workflow

Lecture 257 Putting it all Together and Testing the Multimodal Recommender RAG System

Lecture 258 Adding a Streamlit UI to the Multimodal Recommender System

Section 21: AI Agents & Agentic Workflows - Deep Dive

Lecture 259 AI Agents Deep Dive - A Full Overview

Lecture 260 Agents Characteristics and Use Cases

Lecture 261 Download Source Code for AI Agents Section

Lecture 262 Building our First AI Agent - Project Setup (OpenAI API)

Lecture 263 Build our First AI Agent - Creating the Agent Class and Prompt

Lecture 264 First AI Agent - Running our First Agent and Seeing the Results

Lecture 265 Passing Complex Queries Through the Agent

Lecture 266 First Agent - Using a Loop to Automate our Agent

Lecture 267 Adding Interactive to Our Agent - Console App

Lecture 268 Agent Introduction - Section Summary

Lecture 269 LangGraph - Overview & Key Concepts

Lecture 270 LangGraph - How It Helps Build AI Agents

Lecture 271 LangGraph Core Concepts - Simple Flow Diagrapm

Lecture 272 LangGraph - Data and State - Overview

Lecture 273 Building a Simple Agent with LangChain

Lecture 274 LangGraph Simple Bot - Streaming Values - Console App

Lecture 275 Adding Tools to our Basic LangGraph Agent

Lecture 276 Adding tools to the Agent - Part 1

Lecture 277 Adding Tools to the Agent - Using Built-in Tools - Part 2

Lecture 278 Adding Memory to Our Agent State

Lecture 279 Adding Human-in-the-loop to the AI Agent

Lecture 280 Building AI Agents with LangChain - Section Summary

Lecture 281 Hands-on - Build a Financial Report Writer AI Agent

Lecture 282 Agent State and Prompts Setup

Lecture 283 Creating All Nodes - Functions

Lecture 284 Adding Nodes and Edges and Running our Agent

Lecture 285 Adding a GUI to the Agent with Streamlit

Lecture 286 Optimization Techniques - Overview

Lecture 287 Financial Report Writer AI Agent - Course Summary

Section 22: Fine-tuning LLMs

Lecture 288 Fine-tuning Introduction - Overview

Lecture 289 Fine-tuning Techniques - Overview

Lecture 290 Fine-tuning Comparison of Techniques

Lecture 291 Fine-tuning General Process - Overview

Lecture 292 Fine-tuning OpenAI Models Pricing

Lecture 293 Tokens and the Tokenizer OpenAI Tool

Lecture 294 HANDS-ON - Fine-tuning an OpenAI Model - Full Walkthrough

Lecture 295 Crating a Chatbot with our Fine-tuned Model and Testing

Section 23: Fine-Tuning Technique - LoRA Deep Dive

Lecture 296 LoRA Introduction - Benefits

Lecture 297 LoRA Deep Analysis

Lecture 298 LoRA Implementation Strategy Workflow

Lecture 299 Hands-on - Training Models - LoRA and PEFT

Lecture 300 Running LoRA Model Fine-tuning and Testing

Lecture 301 Creating an API Service to Interface with Our Fine-tuned Models

Lecture 302 Testing our LoRA Model API Endpoint

Lecture 303 Chatting with LoRA Fine-tuned Models

Lecture 304 Full LoRA Workflow - Train and Chat with Fine-tuned Models

Section 24: Wrap up and Next Steps

Lecture 305 Wrap up and Next Steps

Developers looking to implement AI-powered document search and retrieval.,Tech Entrepreneurs & Product Managers who want to build AI-driven applications.,Students & Researchers exploring the practical applications of LLMs and AI-driven automation.