Example Curriculum
Section 1: Introduction to Retrieval Augmented Generation (RAG) Systems
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days
after you enroll
Section 2: Fundamentals of Retrieval Systems
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days
after you enroll
- Overview: Fundamentals of Retrieval Systems (4:04)
- Overview of Information Retrieval (5:37)
- What is Tokenization? (7:20)
- OpenAI Tokenizer (6:12)
- Libraries and Data Handling for RAG (3:15)
- Tokenization Techniques (4:24)
- Preprocessing Steps (8:57)
- Types of Retrieval Systems (7:06)
- Vector Space Model (TF-IDF) (9:44)
- Implementing TF-IDF (6:07)
- TF-IDF Function and Output Analysis (7:56)
- Boolean Retrieval Model (3:57)
- Boolean Retrieval Implementation (16:56)
- Probabilistic Retrieval Model - Part 1 (7:14)
- Probabilistic Retrieval Model - Part 2 (7:28)
- How Google Search Works (11:29)
- Key Concepts: Indexing, Querying, and Ranking (7:22)
- What Did You Learn in This Section? (2:31)
- Let's Have Some Fun (+ More Resources)
Section 3: The Science of Prompt Engineering
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Section 4: Generative AI Fundamentals
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days
days
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- Overview: Generative AI Fundamentals (1:46)
- Introduction to Text Generation (4:09)
- Understanding Transformers (12:47)
- Rock-Paper-Scissors, Dices and Strawberries (8:24)
- Text Generation with GPT2 (12:51)
- Tokenization for Text Generation (6:00)
- Padding the Data for Consistency (5:05)
- Attention Mechanisms (6:14)
- Creating a Dataset Class (7:35)
- Fine-Tuning the GPT-2 Model (8:32)
- Generating Text with GPT-2 (4:13)
- What Did You Learn in This Section? (1:39)
- Unlimited Updates
Section 5: The Science of LLMs
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Section 6: Retrieval-Augmented Generation (RAG) Fundamentals
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days
days
after you enroll
- Overview: RAG Fundamentals (2:38)
- Introduction to RAG Architecture (5:17)
- Tokenization and Embeddings for RAG (13:15)
- FAISS Index: Efficient Similarity Search (4:15)
- Building a Retrieval System (7:44)
- Developing a Generative Model (11:00)
- Implementing the RAG System (7:00)
- Defining a Relevant Context Distance (11:39)
- Understanding Generation Model Parameters (6:22)
- Configuring RAG with Parameters (5:06)
- What Did You Learn in this Section? (3:09)
- Implement a New Life System
Section 7: The Science of RAG
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days
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Section 8: Working with the OpenAI API
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- Overview: Working with the OpenAI API (3:47)
- OpenAI API for Text (8:47)
- Setting Up OpenAI API Key (5:49)
- System Message and Parameters (14:59)
- OpenAI API Setup (4:31)
- Generating Text with OpenAI API (7:02)
- OpenAI API Parameters (10:20)
- OpenAI API for Images (8:14)
- With Image URL (4:54)
- Converting Images to Base64 (3:49)
- Assess My Python Course Thumbnail (4:48)
- What Did You Learn in this Section? (3:50)
Section 9: Project - Customer Acquisition
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days
days
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Section 10: RAG with OpenAI GPT Models
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days
days
after you enroll
- Overview: RAG with OpenAI GPT Models (4:34)
- Case Study Briefing: Cooking Books (4:57)
- Converting PDF to Images (9:15)
- Reading a Single Image with GPT (12:03)
- Enhancing AI with Prompt Engineering (9:10)
- Reading All Images in a Dataset (5:07)
- Filtering Non-relevant Information (6:03)
- Understanding Embeddings in NLP (6:50)
- Generating Embeddings (13:56)
- Building FAISS Index and Metadata Integration (6:27)
- Implementing a Robust Retrieval System (14:41)
- Combining Outputs for Enhanced Results (2:56)
- Constructing a Generative Model (11:42)
- Complete RAG System Implementation (6:41)
- How to Improve RAG Systems Effectively? (7:03)
- Course Check-In
Section 11: Working With Unstructured Data
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days
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- Overview: Working With Unstructured Data (3:36)
- Introduction to Langchain Library (7:26)
- Excel Data: Best Practices for Data Handling (6:41)
- Python - Initial Setup for Data Processing (5:47)
- Loading Data and Implementing Chunking Strategies (5:13)
- Developing a Retrieval System for Unstructured Data (6:10)
- Building a Generation System for Dynamic Content (9:12)
- Building Retrieval and Generation Functions (9:57)
- Working with Word Documents (4:54)
- Setting Up Word Documents for RAG (6:17)
- Implementing RAG for Word Documents (2:26)
- Working with PowerPoint Presentations (4:44)
- PowerPoint Setup for RAG (4:11)
- RAG Implementation for PowerPoint (3:09)
- Working with EPUB Files (4:58)
- EPUB Setup for RAG (4:47)
- RAG Implementation for EPUB Files (2:22)
- Working with PDF Files (4:21)
- PDF Setup for RAG (5:51)
- RAG Implementation for PDF Files (5:37)
- What Did You Learn in This Section? (3:56)
- Exercise: Imposter Syndrome (2:55)
Section 12: Multimodal RAG
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days
days
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- Overview: Multimodal RAG (3:38)
- Introduction to Multimodal RAG (5:58)
- Setup and Video Processing (5:23)
- Extracting Audio from Video (8:44)
- Compressing Audio Files (4:17)
- Transcribing Audio with OpenAI Whisper (10:07)
- Whisper Model (6:31)
- Extracting Frames from Video (5:49)
- Introduction to Contrastive Learning (5:14)
- Understanding the CLIP Model (5:22)
- Tokenizing Text for Multimodal Tasks (8:13)
- Chunking and Embedding Text (11:36)
- Embedding Images for Multimodal Analysis (8:36)
- Understanding Cosine Similarity in Multimodal Contexts (6:46)
- Applying Contrastive Learning and Cosine Similarity (10:26)
- Visualizing Text and Image Embeddings (11:11)
- Query Embedding Techniques (4:12)
- Calculating Cosine Similarity for Query and Text (11:47)
- GenAI Model Setup for Multimodal Tasks (4:55)
- Building a GenAI Model (7:11)
- What Did You Learn in This Section? (2:12)
Section 13: Project - Starbucks Financial Data
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days
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Section 14: Agentic RAG
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days
days
after you enroll
- Overview: Agentic RAG (2:51)
- AI Agents (7:51)
- Agentic RAG (5:44)
- Setup and Data Loading (9:54)
- State Management and Memory in Agentic Systems (7:54)
- AgentState Class (4:29)
- Greeting the Customer (4:52)
- AI Agent that Checks the Question (10:47)
- AI Agent that Assesses the Validity of the question (7:22)
- Retrieving the Documents (5:46)
- Testing the App (7:13)
- Generate Answers (9:21)
- AI Agent that Improves the Answer (11:13)
- Asking User For More Questions (5:29)
- Agentic RAG Recap - Key Learnings and Next Steps (6:17)
Where To Go From Here?
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