AI & LLM Engineering Mastery: GenAI, RAG Complete Guide

Introduction
1 Introduction
2 DEMO What Youll Build in this Course
3 Course Structure
4 How To Get The Most from This Course

Development Environment Setup
5 Development Environment Setup Overview
6 Install Python on Windows for WINDOWS USERS
7 Install Python on MAC for MAC USERS
8 Download Visual Studio Code
9 Install the Python Extension Pack for VS Code
10 Running First Python Program in VS Code

Do You Know Python
11 Python Deep Dive Introduction and Overview

OPTIONAL Python Deep Dive Master Python Fundamentals
12 What is Python and Where Its Used
13 Python Compilation Interpretation Process
14 Download Python Fundamentals Code
15 Declaring Variables in Python
16 Data Types
17 Python fStrings
18 Numbers Integers and Floats
19 Introduction to Lists Accessing and Modifying Them
20 fStrings Individual Values from a List
21 Sorting a List and Getting a List Length
22 Lists and Loops Looping through a List
23 Making a List of Numbers with Loops and the Range Function
24 Statistics Functions for Numbers
25 Generate Even Numbers with the List and Range
26 Important Code Organization Note
27 List Comprehension
28 Tuples
29 Branching If Statements and Booleans
30 The Elif and the in Keywords
31 Handson Using AND and OR Logical Operators
32 AND OR Logical Operators
33 Checking for Inequalities
34 Handson Inner IfStatements
35 Data Structures Dictionaries Introduction and Declaring and Accessing Values
36 Modifying a Dictionary
37 Iterating Through a Dictionary
38 Nested Dictionaries and Looping Through Them
39 Looping through a Dictionary with a List Inside
40 User Input and While Loops User Input Introduction
41 Handson Odd or Even Number
42 While Loops Simple Quit Program
43 Handson Quiz Game
44 Removing all Instances of Specific Values from a List
45 Handson Dream Travel Itinerary Program Filling a Dictionary with User Input
46 Functions Introduction
47 Passing Information to a Function parameters
48 Positional and Named Arguments
49 Default Values Parameters
50 Return Values from a Function
51 Handson Returning an Integer Intro do DocString
52 Functions Passing a List as Argument
53 Passing an Arbitrary Number of Arguments to a Function
54 Introduction to Modules Importing Specific functions from a Module
55 Using the as as an Alias
56 Classes and OOP Object Oriented Programming The init and str methods
57 Adding More Methods to the Class
58 Setting a Default Value for an Attribute
59 Modifying Class Attribute directly and with Methods
60 Inheritance Create an Ebook Child Class
61 Overriding Methods
62 Creating and Importing from a Module
63 The Object Class Overview
64 The Python Standard Library
65 Random Module Random Fruit Handson
66 Handson Random Fruit with Choice Module Method
67 Using Datetime Module
68 Writing Reading Files Do Useful Tasks with Python Do amazing things
69 The Path Class Reading a Text File
70 Resolving Path Reading From a Subdirectory with Path
71 Path Properties Overview
72 Writing to Text file with Path
73 Read and Write to File Using the with Keyword
74 Handling Exceptions
75 The FileNotFound and IndexError Exceptions Types
76 Custom Exception Creation and handling
77 JSON Reading and Writing to a JSON File
78 Handson Writing and Reading Countries to JSON file
79 Handson File Organizer
80 Python Virtual Environment and PIP
81 Setting up Virtual Environment and Installing a Package
82 Handson Watermarker Python Tool
83 Building an Image Watermarker in Python Part 1
84 Generating the Watermarked Images
85 Reading CSV File Introduction
86 Getting the CSV header Position
87 Reading Data from a CSV Column
88 Plotting a Graph with CSV Data

Deep and Machine Learning Deep Dive
89 Deep and Machine Learning Deep Dive Overview and Breakdown
90 Deep Learning Key Aspects
91 Deep Neural Network Dissection Full Dive with Analogies
92 The Single Neuron Computation Deep Dive
93 Wights Deep Dive
94 Activation Functions Deep Dive with Analogies
95 Deep Learning Summary
96 Machine Learning Introduction Machine Learning vs Deep Learning
97 Learning Types Education System Analogy
98 Comparative Capabilities Deep Learning and Machine Learning and AI Summary

Generative AI GenAI Deep Dive
99 GenAI Introduction and Architecture Overview
100 GenAI Key Technologies Limitations and challenges
101 GenAI Key Components Overview and Summary

LLMs Large Language Models Fundamentals A Deep Dive
102 LLMs Overview
103 The Transformer Architecture Fundamentals
104 The SelfAttention Mechanism Analogy
105 The Transformers Library Deep Dive
106 HANDSON Create a Simple LLM from the Transformers Library Simple
107 HANDSON Handson Enhanced Transformers LLM
108 Opensource vs Closedsource Models Overview

OpenAI Models and Setup
109 Setup OpenAI Account and API Key
110 Using APIs Effectively in AI Projects
111 HANDSON Making our First Call to OpenAI Model

Prompt Engineering Communicating with LLMs Deep Dive
112 Prompt Engineering Introduction
113 Prompt Engineering and Types Why it Matters
114 HANDSON Simple Prompting Example
115 Advanced Prompting Techniques and Challenges
116 HANDSON Fewshots Prompting
117 HANDSON Zeroshot Prompting
118 HANDSON ChainofThoughts Prompting
119 HANDSON Instructional Prompting
120 HANDSON RolePlaying and Openended Prompting
121 Temperature and Topp Sampling
122 HANDSON Prompt Techniques Combination and Streaming
123 Prompt Engineering Summary and Takeaways

Ollama OpenSource Models Complete Guide
124 Ollama Introduction
125 Download Source Code and Resources
126 Ollama Deep Dive Ollama Overview What is Ollama and Advantages
127 Ollama Key Features and Use Cases
128 System Requirements Ollama Setup Overview
129 HANDSON Download and Setup Ollama and Llama32 Model
130 Ollama Models Page Overview
131 Ollama Model Parameters Deep Dive
132 Understanding Parameters and Disk Size and Computational Resources Needed
133 Ollama CLI Commands Pull and Testing a Model
134 Pull in the Llava Multimodal Model and Caption an Image
135 Summarization and Sentiment Analysis Customizing Our Model
136 Ollama REST API Generate and Chat Endpoints
137 Ollama REST API Request JSON Mode
138 Ollama Models Support Different Tasks Summary
139 Different Ways to Interact with Ollama Models
140 Ollama Model Running Under Msty App
141 Ollama Python SDK for Building LLM Local Applications
142 HANDSON Interact with Llama3 in Python Using Ollama REST API
143 Ollama Python Library Chatting with a Model
144 Chat Example with Streaming
145 Using Ollama Show Function
146 Create a Custom Model in Code

Context Memory Management for LLMs Deep Dive
147 HANDSON Context and Memory Management Overview
148 What is Context and Memory Management Deep Dive
149 HANDSON Adding Memory and Context to Chatbox
150 Summary

Logging in LLM Applications Deep Dive
151 Logging Introduction What and the Why
152 Logging in LLM Applications and Logging Life Cycle
153 HANDSON Chatbot with Logging
154 Summary

RAG RetrievalAugmented Generation Deep Dive
155 RAG Introduction What is it
156 RAG Key Components The RAG Triad
157 RAG vs Pure GenAI Models
158 RAG Deep Dive Full Diagram Walkthrough
159 RAG Benefits and Practical Applications
160 RAG Challenges
161 RAG Fundamentals Takeaways Summary

Vector Databases and Embeddings Deep Dive
162 Vector Databases and Embeddings for RAG Workflows Introduction
163 Download Source code
164 Introduction to Vector Databases Full Overview
165 Why Vector Databases
166 Vector Databases Benefits and Advantages
167 Traditional vs Vector Databases Limitations and challenges
168 Vector Databases Embeddings Full Overview
169 Embeddings vs Vectors Differences
170 Vector Databases How They Work and Advantages
171 Vector Databases Use Cases
172 Vector and Traditional Databases Summary
173 The Top 5 Vector Databases Overview
174 Building Vector Databases Dev Environment Setup
175 Setup VSCode Python and OpenAI API Key
176 Chroma Database workflow
177 Creating a ChromaDB and Adding Documents and Querying
178 Looping Through the Results Showing Similarity Search Results
179 Chroma Default Embedding Function
180 Chroma Vector Database Persisting Data and Saving
181 Creating an OpenAI Embeddings Raw without Chroma
182 Using OpenAIs Embedding API to Create Embedding in ChromaDB
183 Vector Databases Metrics and Data Structures
184 Summary
185 Vector Similarity Deep Dive Cosine Similarity
186 Eucledian Distance L2 Norm
187 Dot Product
188 Summary
189 Vector Databases and LLM Deep Dive
190 Loading all Documents
191 Generating Embeddings from Documents and Insert to Vector Database
192 Getting the Relevant Chunks when Given a Query
193 Using OpenAI LLM to Generate Response Full Workflow
194 Summary

HANDSON RAG PDF Workflow Build RAG Workflows Deep Dive
195 Building a RAG Pipeline Overview
196 First RAG Workflow Architectural Diagram
197 Setting up the Embedding Model Class
198 HANDSON Building and Showcasing the RAG Workflow
199 HANDSON RAG Workflow with UI Streamlit
200 First RAG Pipeline Summary

HANDSON Build a PDF RAG System with Text Chunking
201 PDF RAG Workflow Architecture Overview
202 PDF and Chunk Processing and Chunk Overlap Deep Dive
203 Setting up the SimpleRAGSystem Class and Methods
204 Testing the PDF RAG System
205 Simple PDF RAG Workflow Summary

LLM Tools and Frameworks LangChain Deep Dive
206 LLM Frameworks Introduction LangChain Fundamentals
207 What is LangChain and and Main Components
208 LangChain Setup and ChatModel
209 Handson LangChain ChatPromptTemplates
210 Indexes Retrievers and Data Preparation Overview
211 HandsOn LangChain TextLoaders
212 Handson Text Splitting and Cleaning
213 Handson Embeddings and Retriever with FAISS VectorStore
214 LangChain TextSplitter Deep Dive
215 LangChain DirectoryLoader
216 LangChain PDFLoader
217 Handson LangChain Chains
218 Handson Simple RAG System with Chat and LangChain Chains
219 Handson Full RAG System QA Bot Using LangChain

HANDSON Building LLM Applications with LangChain
220 LLM Application News Summarizer Architectural Overview
221 News Summarizer Full Implementation
222 LLM Application Youtube Video Summarizer Architectural Overview
223 Youtube Video Summarizer QA Dependency Setup
224 Youtube Video Summarizer Class Setup and Walkthrough
225 Youtube Video Summarizer QA Testing the Workflow
226 LLM Application Voice Assistant RAG System Architectural Overview
227 Voice Assistant RAG System Demo
228 Voice Assistant RAG System Walkthrough and Demo

Advanced RAG Techniques Naive vs Advanced RAG Techniques
229 RAG and the RAG Triad Quick Overview and Recap
230 What is RAG and Naive RAG Overview and Pitfalls Motivation
231 Deep Dive into Each Naive RAG Drawbacks
232 Advanced RAG Technique Query Expansion with Multiple Queries Overview
233 Handson Query Expansion with Multiple Queries Generate Multiple Queries
234 Query Expansion Workflow Architectural Diagram
235 Handson Setting up the Workflow and Code Walkthrough
236 Query Expansion Full RAG Workflow
237 Query Expansion with Multiple Queries Downsides Summary
238 ReRanking Crossencoder and Biencoders Overview
239 Reranking Technique RAG System Workflow Architecture
240 Cohere Rerank API Key Setup
241 Handson Reranking Implementation with Cohere Full Implementation
242 Reranking Summary

Multimodal RAG Deep Dive
243 Multimodal RAG Source Code
244 RAG Multimodal RAG Recap and Overview
245 RAG Benefits and Practical Applications
246 Multimodal RAG Overview Motivation and Benefits How it Works
247 How Search Is Integrated into a Multimodal RAG System Full Workflow
248 Why Multimodal Search is so Powerful
249 Visual Explanation Why Multimodal Search is so Powerful
250 HANDSon Multimodal Search System setup Create Embeddings from Images
251 Finish the Multimodal Search System
252 HANDSON Multimodal Recommender System Overview
253 Getting our Dataset from HuggingFace showing Number of Rows
254 Saving Images Embeddings to Vector Database
255 Testing our MultiModal Recommender System Fetching the Correct Images
256 Setting up the RAG Workflow
257 Putting it all Together and Testing the Multimodal Recommender RAG System
258 Adding a Streamlit UI to the Multimodal Recommender System

AI Agents Agentic Workflows Deep Dive
259 AI Agents Deep Dive A Full Overview
260 Agents Characteristics and Use Cases
261 Download Source Code for AI Agents Section
262 Building our First AI Agent Project Setup OpenAI API
263 Build our First AI Agent Creating the Agent Class and Prompt
264 First AI Agent Running our First Agent and Seeing the Results
265 Passing Complex Queries Through the Agent
266 First Agent Using a Loop to Automate our Agent
267 Adding Interactive to Our Agent Console App
268 Agent Introduction Section Summary
269 LangGraph Overview Key Concepts
270 LangGraph How It Helps Build AI Agents
271 LangGraph Core Concepts Simple Flow Diagrapm
272 LangGraph Data and State Overview
273 Building a Simple Agent with LangChain
274 LangGraph Simple Bot Streaming Values Console App
275 Adding Tools to our Basic LangGraph Agent
276 Adding tools to the Agent Part 1
277 Adding Tools to the Agent Using Builtin Tools Part 2
278 Adding Memory to Our Agent State
279 Adding Humanintheloop to the AI Agent
280 Building AI Agents with LangChain Section Summary
281 Handson Build a Financial Report Writer AI Agent
282 Agent State and Prompts Setup
283 Creating All Nodes Functions
284 Adding Nodes and Edges and Running our Agent
285 Adding a GUI to the Agent with Streamlit
286 Optimization Techniques Overview
287 Financial Report Writer AI Agent Course Summary

Finetuning LLMs
288 Finetuning Introduction Overview
289 Finetuning Techniques Overview
290 Finetuning Comparison of Techniques
291 Finetuning General Process Overview
292 Finetuning OpenAI Models Pricing
293 Tokens and the Tokenizer OpenAI Tool
294 HANDSON Finetuning an OpenAI Model Full Walkthrough
295 Crating a Chatbot with our Finetuned Model and Testing

FineTuning Technique LoRA Deep Dive
296 LoRA Introduction Benefits
297 LoRA Deep Analysis
298 LoRA Implementation Strategy Workflow
299 Handson Training Models LoRA and PEFT
300 Running LoRA Model Finetuning and Testing
301 Creating an API Service to Interface with Our Finetuned Models
302 Testing our LoRA Model API Endpoint
303 Chatting with LoRA Finetuned Models
304 Full LoRA Workflow Train and Chat with Finetuned Models

Wrap up and Next Steps
305 Wrap up and Next Steps