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Mastering Generative AI: From Basics to Advanced Techniques

Master Generative AI: From Basics to Breakthroughs in AI Models and RAG Systems

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What you'll learn

Understand the evolution of generative AI from classical to modern techniques
Apply RAG and LangChain for building scalable AI applications
Implement end-to-end pipelines using LlamaIndex and LLM fine-tuning methods
Deploy AI models with CI/CD pipelines and container orchestration tools

This course includes

104 hours of video
Certificate of completion
Access on mobile and TV

Course content

1 modules • 109 lessons • 104 hours of video

Mastering Generative AI: From Basics to Advanced Techniques

109 lessons • 104 hours

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Generative AI In-Depth Roadmap from Beginner to Expert #generativeai #artificialintelligence31:32
Generative AI Complete History Part-1 | Classical AI vs Modern AI | AI vs ML vs DL vs GEN AI01:03:56
Generative AI History Part2 | Language Modelling | Seq to Seq model | RNN | LSTM | GRU59:25
Generative AI history Final Part (part3) | Transformer | LLM | Chatgpt Training | Diffusion Model54:44
@LlamaIndex Introduction | RAG System | LlamaIndex Doc Walkthrough #generativeai #llamaindex #llm29:50
@LlamaIndex Project Setup | Simple Q/A System using OpenAI API and LlamaIndex #OpenAI #LlamaIndex31:31
Google Gemini Introduction Part 1 | Google Gemini Python API #gemini #generativeai #llm36:00
Google gemini API with Python Part 2| text to text generation | image to text generation #gemini #ai31:39
Google gemini API with Python Part 3| Embedding | Saftey Setting #gemini01:02:07
🟥 Let’s build QA System with @LlamaIndex and Google Gemini!(LlamaIndex, Gemini Embedding, GeminiPro)02:22:20
@OpenAI "SORA" Just SHOCKED EVERYONE | Text-to-Video Generation | AGI | Alternatives of SORA29:36
LangChain v/s Llama-Index | Detailed Differences | Which one you should use?38:33
End to End RAG Pipeline Part-1 | RAG Architecture | Ingestion | generation | Reterival #rag #llm53:45
End to End RAG Pipeline Part-2 | Advance Reterival Process | RAG Architecture In depth37:02
RAG Pipeline from Scratch Using OLlama Python & Llama2 | | Llama2 Setup in local PC #llama2 #rag49:03
RAG Application using @LangChain @OpenAI and FAISS #llm #rag #python #langchain #vectordata53:16
RAG Application using Langchain Mistral AI and Weviate db #llm #rag #langchain #vector #mistral47:30
RAG Application Using OpenSource Framework @LlamaIndex and @Mistral-AI #rag #finetuning #llm33:23
Haystack by Deepset - Framework to Build LLM Apps | RAG Pipeline Using Haystack and OpenAI46:02
Discover The Power Of Multilingual Ai Voice Assistant With Google Gemini-pro And gTTS Technology!45:29
End to End RAG Application Using Haystack MistralAI Pinecone & FastAPI #rag #llm #haystack #mistral01:01:40
Complete Automated Local Setup for AI (ML,DL,GenAI) Development With Vscode, Git, Anaconda & Docker47:42
25 Best VSCode Extensions for AI (ML,DL,GenAI) Devlopment In 202422:47
Multimodal RAG Systems: Comprehensive Introduction to Next-Gen AI Technology #multimodal #rag #ai24:36
MultiModal RAG Application Using LanceDB and LlamaIndex for Video Processing46:35
Realtime Multimodal RAG Usecase Part 1 | Extract Image,Table,Text from Documents #rag #multimodal41:27
Realtime Multimodal RAG Usecase Part 2 | MultiModal Summrizer | RAG Application #rag #multimodal #ai29:42
Realtime Multimodal RAG Usecase Part 3 | MultiVectorRetriever with Langchain | RAG Application #rag21:44
Realtime Multimodal RAG Usecase with Google Gemini-Pro-Vision and Langchain | RAG Application #rag40:30
End to End RAG App with Hugging face Google Gemma & MongoDB Vector Search #rag #ai #llm #genai53:23
Building Real-Time RAG Pipeline With Mongodb and Pinecone Part-1 #rag #llm #mongodb #pinecone44:38
Building Real-Time RAG Pipeline With Mongodb and Pinecone Part-2 #rag #llm #mongodb #pinecone24:45
Chat With Multiple Documents(pdfs, docs, txt, pptx etc.) using AstraDB and Langchain #rag #ai31:43
Built Powerful Multimodal RAG using Vertex AI(GCP), AstraDb and Langchain #rag #ai37:45
End to end E-Commerce Chatbot With AWS Deployment using Astra dB(Cassandra), Langchain & Open AI #ai01:08:18
Realtime Powerful RAG Pipeline using Neo4j(Knowledge Graph Db) and Langchain #rag53:17
Advance RAG 01 - Powerful RAG Using Hybrid Search(Keyword+vVector search) | Ensemble Retrieval01:01:10
Advacne RAG 02 - Hybrid Search (Keyword + Vector ) & Reranking With Cohere API | Ensemble Retrieval48:16
End-to-End Weather Chatbot with Google DialogFlow and AWS CI/CD Deployment02:35:30
Advanced RAG 03 - Reranking with Sentence Transformers and BM25 API28:42
Advanced RAG 04 - Reranking with Cross Encoders, and Cohere API19:21
Advance RAG 05 - Merger Retriever and LongContextReorder | Lost in Middle Phenomenon40:11
Advance RAG 06- RAG Fusion (Get More Relevant Results for Your RAG) | Reranking With RRF48:59
Advance RAG 07 - Flash Reranker for Superfast Reranking29:47
Advance RAG 08- Powerful RAG with Langchain Contextual Compression Retriever #ai #llm #openai57:20
Advance RAG 09- Powerful RAG with Self Querying Retriever #ai #llm #openai45:10
Advance RAG 10- Powerful RAG with Parent Document Retriever #ai #llm #openai #gemini51:04
End-to-End RAG With Llama 3.1, Langchain, FAISS and OLlama #ai #llm #llama #huggingface38:04
Advance RAG 11- Powerful RAG with Sentence Window Retriever using @LlamaIndex and @qdrant #ai #llm39:47
Advance RAG 12- Powerful RAG with Merger Retriever and Hypothetical Document Embeddings(HyDE) #ai31:52
Complete @LangChain Essential in 1 shot | LangChain Core | LangServe | LangGraph | LangSmith | Agent01:55:49
Chatbot Using @LangChain With Memory(Chat History) | LangChain Core | LangSmith01:07:50
RAG Based Chatbot With Memory(Chat History) | Creating History Aware Retriever | Langchain #ai #rag37:27
Langchain Conversation Buffer Memory vs Conversation Buffer Window Memory | Chat History#ai #llm #yt40:44
Langchain Conversation Entity Memory | Langchain Memory Class | Chat History#ai #llm #yt #chatbot33:00
Langchain Conversation Summary Memory vs Conversation Summary Buffer Memory | Chatbot #ai #llm #rag44:11
LangChain Expression language(LCEL) for Chaining the Components | All Runnables | Async & Streaming52:49
LangGraph 01: Syllabus Introduction of End to End LangGraph Course | LangChain #ai #genai #llm10:34
LangGraph:02 LangGraph Course Pre-requist | AI Assistant | RAG I LCEL | Tool & Agent #ai #genai #llm42:41
LangGraph:03 LangChain AI Agents | Tools | Tool Calling Agent | ReAct Agents #genai #llm #aiagent39:15
LangGraph:04 LangChain ReAct Agent with Custom Tool and Self-Ask Agent with Search | AI Agents #llm52:15
LangGraph:05 Building AI Agent from Scratch Using Python with Custom Tool #llm #genai #ai #aiagents45:31
LangGraph:06 Detailed Introduction of LangGraph #llm #genai #ai #aiagents19:49
LangGraph:07 Code LangGraph From Scratch | LangGraph Deep Dive #llm #genai #ai #aiagents #langchain27:10
LangGraph:08 Adding RAG to LangGraph Workflow | LangGraph Deep Dive #llm #genai #aiagents #langchain37:49
LangGraph:09 End to End Chatbot using LangGraph With Memory #llm #genai #aiagents #langchain #ai46:03
LangGraph:10 Structured Output with LangGraph Agents #llm #genai #aiagents #langchain #ai26:18
LangGraph:11 Building Finance Bot with LangGraph's ReAct Agent #llm #genai #aiagents #langchain #ai30:54
LangGraph:12 LangGraph Agent with Human-In-The-Loop, Checkpoints & Breakpoints #llm #genai #aiagents44:22
LangGraph:13 Corrective RAG for Real Time AI Application #llm #genai #aiagents #ai #langchain #genai42:35
LangGraph:14 Agentic RAG for Real Time Agentic AI Application #llm #genai #aiagents #ai #genai01:06:10
LangGraph:15 Self-RAG for Real Time Agentic AI Application #llm #genai #aiagents #ai #genai59:34
Roadmap of Agentic AI & Generative AI with 150 + Interview Questions and Answers #ai #genai #llm17:36
LangGraph:16 Advance SQL Database Agent Powered by LangGraph #llm #genai #aiagents #ai #genai37:07
🟥 Autogen Research Agent: End-to-End Project for Paper Analysis & Summarization01:35:30
LangGraph:17 Introduction to Multi-Agent System #llm #genai #aiagents #ai #genai #agent01:07:10
LangGraph:18 Network or Collaborative Multi-Agent System Implementation #aiagents #ai #genai #agent52:37
LangGraph:19 Research and Analysis with Collaborative Multi-Agent System #aiagents #ai #genai #agent36:43
LangGraph:20 Supervisor Multi-Agentic System | Agentic AI #aiagents #ai #genai #agent #generativeai45:54
LangGraph:21 End-to-End Supervisor Multi-Agentic AI Project for Booking Doctors Appointment #aiagent01:09:20
LLM Fine-Tuning: 01 LLM Fine-Tuning From Scratch—Full Playlist Coming Your Way #aiagents #finetuning20:01
LLM Fine-Tuning: 02 Understanding Model Pretraining and Training in AI #aiagents #finetuning #ai01:04:40
🟥 Live Q&A on Generative & Agentic AI—Ask Me Anything!37:45
LLM Fine-Tuning 03: Transfer Learning and Model Fine-Tuning #aiagents #finetuning #ai01:11:34
LLM Fine-Tuning 04: Top 10 LLM Fine-Tuning Frameworks for 2025 | Best Tools for Finetuning AI Agents47:57
LLM Fine-Tuning 05: Fine-Tuning vs. RAG vs. AI Agents — Which Approach Fits Your Use Case?27:46
LLM Fine-Tuning 06: Why Finetuning Was Difficult in RNN or LSTM – How Transformers Changed the Game50:34
Advance RAG Course: Master All RAG Retrieval & Reranking Techniques in One Video💡!08:05:38
LLM Fine-Tuning 07: LSTM vs Transformer | Why Transformers Replaced LSTM in NLP48:36
LLM Fine-Tuning 08: Master Hugging Face in 3 Hours | Full Crash Course 2025 #ai #huggingface #llm03:10:34
LLM Fine-Tuning 09: Fine-Tuning BERT for NLP (NER, Sentiment, QA) | Hugging Face #huggingface #llm58:20
LLM Fine-Tuning 10: LLM Knowledge Distillation | How to Distill LLMs (DistilBERT & Beyond) Part 101:03:40
LLM Fine-Tuning 11: LLM Knowledge Distillation | How to Distill LLMs (LLAMA, Phi & Beyond) Part 201:12:41
LLM Fine-Tuning 12: LLM Quantization Explained( PART 1) | PTQ, QAT, GPTQ, AWQ, GGUF, GGML, llama.cpp02:12:20
LLM Fine-Tuning 13: LLM Quantization Explained (PART 2) | PTQ, QAT, GPTQ, AWQ, GGUF, GGML, llama.cpp03:21:12
LLMOPS 01: End-to-End Advanced RAG Project with LLMOPS | Complete Setup & Use Cases Discussion23:59
LLMOPS 02: Build Multi-Doc Chat with Advanced RAG Part-1| RAG in a Modular Manner (Logger, Config)01:22:08
LLMOPS 02: RAG Analysis & Evaluation Strategy Part-2 | Advanced RAG Pipeline in LLMOPS53:55
LLMOPS 03: Building API with FastAPI & Swagger Testing | API Development in LLMOPS Project43:47
LLMOPS 04: Building UI & Testing the Full App | Frontend Integration in LLMOPS Project14:04
LLMOPS 05: Unit & Integration Testing with Pytest | Hands-on Testing in LLMOPS Project32:44
LLMOPS 06: CI/CD Deployment with AWS ECS & Fargate | End-to-End GenAI Project Deployment01:15:47
Guardrails for LLM Applications | Complete Tutorial for AI Developers WIth Guardrails AI01:26:11
LLMOPS 07: Jenkins with Docker | Full CI/CD Pipeline Setup | End-to-End GenAI Project Deployment35:09
LLM Fine-Tuning 14: Train LLMs on Your PDF/Text Data | Domain-Specific Fine-Tuning with Hugging Face01:44:41
LLM Fine-Tuning 15: Instruction Fine-Tuning Explained | Domain-Specific FineTuning with Hugging Face55:52
LLMOPS 09: CI/CD Deployment for LLMOps using GitHub Action on AWS EKS | Deploy LLMOPS Project57:40
LLM Fine-Tuning 16: Preference Alignment & Preference Training in LLMs with RLHF, RLAIF, DPO, LoRA59:38
LLM Fine-Tuning Crash Course: Finetune model on PDFs, Instruction FT, Preference Training (DPO/RLHF)03:36:13