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

Requirements

No 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.

Homepage:

https://www.udemy.com/course/llm-engineering/