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Generative AI application design and devlopement
Introduction
Introductions (1:09)
Get the most from this course (5:08)
Setup development environment
Section overview (1:30)
Setup development environment (100-v2) (3:14)
Install Python UV tool (110-v2) (6:06)
Setup environment : Anaconda (120-v2) (8:31)
Setup environment : UV (130-v2) (2:57)
Quizzes, Exercises, and Projects (200) (5:40)
Accessing the Large Language Models (5:36)
Generative AI : Fundamentals
Section overview (1:35)
Intro to AI, ML, Neural networks, and Gen AI (200) (8:40)
Neurons, neural & deep learning networks (300) (11:51)
Exercise: Try out a neural network for solving math equations (400) (9:52)
A look at generative AI model as a black box (500) (9:16)
Quiz: Fundamentals of Generative AI models (600) (4:47)
An overview of generative AI applications (700) (8:28)
Exercise: Setup access to Google Gemini models (800) (12:20)
Introduction to Hugging Face (900) (7:24)
Exercise: Checkout the Hugging Face portal (1000) (9:57)
Exercise: Join the community and explore Hugging Face (1100) (4:54)
Quiz: Generative AI and Hugging Face (1200) (5:16)
Intro to natural language processing (NLP, NLU, NLG) (1300) (11:46)
NLP with Large Language Models (1400) (10:46)
Exercise: Try out NLP tasks with Hugging Face models (1500) (2:28)
Quiz: NLP with LLMs (1600) (3:08)
Generative AI applications
Section overview (2:50)
Introduction to OLlama (7:06)
OLlama model hosting (9:41)
Model naming scheme (225) (3:34)
Instruct, Embedding and Chat models (250) (11:30)
Quiz: Instruct, Embedding and Chat models (300) (3:35)
Next word prediction by LLM and fill mask task (400) (7:48)
Model inference control parameters (500) (3:26)
Randomness control inference parameters (600) (10:26)
Exercise: Setup Cohere key and try out randomness control parameters (700) (3:05)
Diversity control inference parameters (800) (5:13)
Output length control parameters (900) (7:18)
Exercise: Try out decoding or inference parameters (1000) (2:40)
Quiz: Decoding hyper-parameters (1100) (4:43)
Introduction to In-Context Learning (1200) (12:04)
Quiz: In-context learning (1300) (3:33)
Hugging Face Models : Fundamentals
Section overview (1:38)
Exercise: Install & work with Hugging Face transformers library (200) (5:48)
Transformers library pipeline classes (300) (9:50)
Quiz: HuggingFace transformers library (400) (3:35)
HuggingFace hub library & working with endpoints (500) (11:04)
Quiz: HuggingFace hub library (600) (3:16)
Exercise: PoC for summarization task (700) (7:22)
HuggingFace CLI tools and model caching (800) (4:31)
Hugging Face Models : Advanced
Model input/output and Tensors (900) (6:15)
HuggingFace model configuration classes (1000) (5:48)
Model tokenizers & Tokenization classes (1100) (10:03)
Working with logits (1200) (7:31)
HuggingFace models auto classes (1300) (5:30)
Quiz: HuggingFace classes (1400) (2:59)
Exercise: Build a question answering system(1500) (10:45)
LLM challenges & prompt engineering
Section overview (2:07)
Challenges with Large Language Models (200) (11:09)
Model grounding and conditioning (300) (10:52)
Exercise: Explore the domain adapted models (350) (3:44)
Prompt engineering and practices (1 of 2) (400) (4:49)
Prompt engineering and practices (2 of 2) (405) (6:59)
Quiz & Exercise: Prompting best practices (500) (3:33)
Few shots & zero shot prompts (600) (4:52)
Quiz & Exercise: Few shot prompts (700) (5:36)
Chain of thought prompting technique (800) (7:23)
Quiz & Exercise: Chain of thought (900) (4:32)
Self consistency prompting technique (1000) (5:33)
Tree of thoughts prompting technique (1100) (8:08)
Quiz & Exercise: Tree of thought (1200) (4:05)
Exercise: Creative writing workbench (v1) (1300) (5:09)
Langchain : Prompts, Chains & LCEL
Section overview (1:28)
Prompt templates (200) (7:04)
Few shot prompt template & example selectors (250) (8:27)
Prompt model specificity (300) (7:07)
LLM invoke, streams, batches & Fake LLM (400) (9:11)
Exercise: Interact with LLM using LangChain (500) (2:51)
Exercise: LLM client utility (600) (4:17)
Quiz: Prompt templates, LLM, and Fake LLM (650) (3:30)
Introduction to LangChain Execution Language (700) (10:07)
Exercise: Create compound sequential chain (800) (1:42)
LCEL : Runnable classes (1 of 2) (900) (6:13)
LCEL: Runnable classes (2 of 2) (1000) (6:41)
Exercise: Try out common LCEL patterns (1100) (1:24)
Exercise: Creative writing workbench v2 (1200) (2:05)
Quiz: LCEL, Chains and Runnables (1300) (4:22)
Handling structured responses
Section overview (1:22)
Challenges with structured responses (200) (8:06)
Langchain output parsers (300) (11:20)
Exercise: Use the EnumOutputParser (400) (3:49)
Exercise: Use the PydanticOutputParser (500) (3:46)
Project: Creative writing workbench (600) (4:42)
Project: Solution walkthrough (1 of 2) (700) (2:40)
Project: Solution walkthrough (2 of 2) (800) (3:57)
Handling parsing errors (900) (9:02)
Quiz and Exercise: Parsers, error handling (1000) (3:16)
Datasets for Training, and Testing
Section overview (0:56)
Dataset for LLM pre-training (200) (5:47)
HuggingFace datasets and datasets library (300) (5:59)
Exercise: Use features of datasets libray (400) (7:49)
Exercise: Create and publish a dataset on Hugging Face (500) (2:25)
Vectors, embeddings & semantic search
What is the meaning of contextual understanding? (200) (8:42)
Building blocks of Transformer architecture (300) (7:18)
Intro to vectors, vector spaces and embeddings (400) (10:11)
Measuring the semantic similarity (500) (6:42)
Quiz: Vectors, Embeddings, Similarity (600) (3:55)
Sentence transformer models (SBERT) (700) (5:55)
Working with sentence transformers (800) (8:45)
Exercise: Work with classification and mining tasks (900) (4:39)
Creating embedding with LangChain (1000) (10:50)
Exercise: CacheBackedEmbeddings classes (1100) (3:15)
Lexical, semantic and kNN search (1200) (9:26)
Search efficiency and search performance metrics (1300) (10:53)
Search algorithms, indexing, ANN, FAISS (1400) (11:11)
Quiz & Exercise: Try out FAISS for similarity search (1500) (9:27)
Search algorithm: Local Sensitivity Hashing (LSH) (1600) (7:51)
Search algorithm: Inverted File Index (IVF) (1700) (7:44)
Search algorithm: Product Quantization (PQ) (1800) (10:41)
Search algorithm: HNSW (1 of 2) (1900) (8:40)
Search algorithm: HNSW (2 of 2) (1950) (11:06)
Quiz & Exercise: Search algorithms & metrics (2000) (6:12)
Project: Build a movie recommendation engine (2100) (6:04)
Benchmarking ANN algorithms (2200) (8:12)
Exercise: Benchmark the ANN algoithms (3:17)
Vector Databases
Challenges with semantic search libraries (200) (6:13)
Introduction to vector databases (300) (12:31)
Exercise: Try out ChromaDB (400) (9:12)
Exercise: Custom embeddings (500) (1:29)
Chunking, symmetric & asymmetric searches (600) (9:48)
LangChain document loaders (700) (7:24)
LangChain text splitters for chunking (800) (9:45)
LangChain retrievers & vector stores (900) (10:20)
Seach scores and maximal-marginal-relevancy (MMR) (1000) (9:38)
Project: Pinecone adoption @ company (1100) (3:44)
Quiz: Vector databases, chunking, text splitters (1400) (4:36)
Conversation User Interface
Introduction to Streamlit framework (200) (9:36)
Exercise: Build a HuggingFace LLM playground (300) (5:09)
Building conversational user interfaces (400) (7:07)
Exercise: Build a chatbot with Streamlit (500) (7:44)
LangChain conversation memory (600) (8:16)
Quiz & Exercise: Building chatbots with LangChain (700) (5:27)
Project: PDF document summarizer application (800) (3:57)
Advanced Retrieval Augmented Generation
Introduction to Retrieval Augmented Generation (RAG) (200) (8:49)
LangChain RAG pipelines (300) (8:59)
Exercise: Build smart retriever with LangChain (400) (1:57)
Quiz: RAG and Retrievers (450) (3:25)
Pattern: Multi query retriever (MQR) (500) (5:32)
Pattern: Parent document retriever (PDR) (600) (9:25)
Pattern: Multi vector retriever (MVR) (700) (6:22)
Quiz: MQR, PDR and MVR (750) (4:36)
Ranking, Sparse, Dense & Ensemble retrievers (800) (10:35)
Pattern: Long context reorder (LCR) (900) (6:56)
Quiz: Ensemble & Long Context Retrievers (950) (4:03)
Pattern: Contextual compressor (1000) (6:48)
Pattern: Merger retriever (1100) (4:42)
Quiz: Contextual compressors and Merger retrievers (1150) (3:13)
Agentic RAG
Introduction to agents, tools and agentic RAG (200) (9:18)
Exercise: Build a single step agent without LangChain (300) (9:36)
Langchain tools and toolkits (400) (12:45)
Quiz: Agents, tools & toolkits (500) (4:31)
Exercise: Try out the FileManagement toolkit (600) (1:16)
How do we humans & LLM think? (700) (5:14)
ReACT framework & multi-step agents (800) (12:09)
Exercise: Build question/answering ReACT agent (900) (10:19)
Exercise: Build a multi-step ReAct agent (1000) (6:59)
LangChain utilities for building agentic-RAG solutions (1100) (11:09)
Exercise: Build an agentic-RAG solution using LangChain (1200) (5:53)
Quiz: Agentic RAG and ReAct (1300) (6:18)
Model Context Protocol (MCP)
Section overview (2:07)
Introduction to-MCP (1 of 2)(200) (11:08)
Exercise: Install MCP server on Claude desktop (300) (3:11)
Introduction to MCP (2 of 2)(400) (10:34)
Quiz: Fundamentals of MCP (450) (2:46)
Exercise : Setup project with MCP Python SDK (500) (2:55)
Exercise : Explore MCP using the Inspector tool (600) (4:11)
Exercise : Code an MCP Server (700) (4:40)
MCP messaging and transport (800) (12:46)
Exercise : Setup standalone MCP server with notifications (900) (3:59)
Quiz: MCP Servers (1000) (3:27)
MCP : Client (1100) (5:06)
Exercise: Create an MCP client class (1200) (7:18)
Exercise: Question-Answering application (1300) (3:11)
Quiz: MCP Clients (1400) (3:11)
LangChain MCP adapter library (6:43)
Exercise: Create tool calling agent with MCP tools (3:44)
Exercise: Connect with Multiple MCP servers (tools) (2:22)
Fine tuning
Introduction to Fine-tuning (200) (4:18)
Fine-tuning : Reasons (300) (7:14)
Fine tuning process (400) (9:03)
Tools for fine tuning (500) (9:37)
Exercise: Fine tune Cohere model for toxicity classification (600) (9:00)
Creating a dataset for fine tuning (800) (12:04)
Exercise: Prepare a dataset and fine tune Open AI 4o model (900) (5:41)
Project: Build a credit card fraud detection dataset (1100) (9:19)
Data preparation for fine-tuning
Introduction to Alpaca (300) (9:45)
Fine-tuning dataset formats (400) (5:01)
Exercise: Explore instruct fine-tuning datasets (500) (1:34)
Fine-tuning datasets for chats (600) (5:58)
Exercise: Prepare chat dataset for fine-tuning (700) (5:52)
Quiz: Fine-tuning datasets (800) (4:42)
Pre-training & Fine-tuning with HuggingFace Trainer
Fine-tuning under the covers (200) (10:44)
Hyperparameters : part 1 of 2 (300) (8:18)
Hyperparameters : part 2 of 2 (350) (9:07)
Understand the checkpointing (400) (3:15)
Exercise: Explore Hyperparameters (500) (6:40)
Intro HuggingFace Trainer, Data collator classes (600) (7:12)
Exercise: Try out the DataCollators (700) (4:04)
Exercise: Pre-training Roberta model (800) (10:05)
Exercise: Full fine-tune BERT for sentiment analysis (900) (10:27)
Quiz: Hyperparameters & Fine-tuning (1000) (4:29)
Quantization
LLM training compute needs (200) (11:03)
Inferencing compute needs (300) (9:01)
Quiz : Check your understanding of GPU & CUDA (400) (2:49)
Introduction to Quantization (500) (8:05)
Exercise: Quantization maths (Affine technique) (4:03)
Applying quantization : Static & Dynamic (8:26)
Exercise: Dynamic quantization with PyTorch (800) (5:02)
Exercise: Static quantization with AutoGPTQ (900) (4:00)
Quiz: Check your understanding of quantization (3:24)
Exercise: Connect with Multiple MCP servers (tools)
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