Generative AI from Basic to Advance
Master Generative AI: From Basics to Breakthroughs in AI Models and RAG Systems
5.0
(2)
26 learners
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
- 124.5 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 125 lessons • 124.5 hours of video
Mastering Generative AI: From Basics to Advanced Techniques
125 lessons
• 124.5 hours
Mastering Generative AI: From Basics to Advanced Techniques
125 lessons
• 124.5 hours
- Generative AI In-Depth Roadmap from Beginner to Expert #generativeai #artificialintelligence 31:33
- Generative AI Complete History Part-1 | Classical AI vs Modern AI | AI vs ML vs DL vs GEN AI 01:03:56
- Generative AI History Part2 | Language Modelling | Seq to Seq model | RNN | LSTM | GRU 59:25
- Generative AI history Final Part (part3) | Transformer | LLM | Chatgpt Training | Diffusion Model 54:44
- @LlamaIndex Introduction | RAG System | LlamaIndex Doc Walkthrough #generativeai #llamaindex #llm 29:50
- @LlamaIndex Project Setup | Simple Q/A System using OpenAI API and LlamaIndex #OpenAI #LlamaIndex 31:32
- Google Gemini Introduction Part 1 | Google Gemini Python API #gemini #generativeai #llm 36:01
- Google gemini API with Python Part 2| text to text generation | image to text generation #gemini #ai 31:40
- Google gemini API with Python Part 3| Embedding | Saftey Setting #gemini 01:02:08
- đ„ Letâs 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 SORA 29:37
- 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 #llm 53:45
- End to End RAG Pipeline Part-2 | Advance Reterival Process | RAG Architecture In depth 37:02
- RAG Pipeline from Scratch Using OLlama Python & Llama2 | | Llama2 Setup in local PC #llama2 #rag 49:04
- RAG Application using @LangChain @OpenAI and FAISS #llm #rag #python #langchain #vectordata 53:16
- RAG Application using Langchain Mistral AI and Weviate db #llm #rag #langchain #vector #mistral 47:30
- RAG Application Using OpenSource Framework @LlamaIndex and @Mistral-AI #rag #finetuning #llm 33:24
- Haystack by Deepset - Framework to Build LLM Apps | RAG Pipeline Using Haystack and OpenAI 46:03
- Discover The Power Of Multilingual Ai Voice Assistant With Google Gemini-pro And gTTS Technology! 45:30
- End to End RAG Application Using Haystack MistralAI Pinecone & FastAPI #rag #llm #haystack #mistral 01:01:40
- Complete Automated Local Setup for AI (ML,DL,GenAI) Development With Vscode, Git, Anaconda & Docker 47:42
- 25 Best VSCode Extensions for AI (ML,DL,GenAI) Devlopment In 2024 22:47
- Multimodal RAG Systems: Comprehensive Introduction to Next-Gen AI Technology #multimodal #rag #ai 24:37
- MultiModal RAG Application Using LanceDB and LlamaIndex for Video Processing 46:36
- Realtime Multimodal RAG Usecase Part 1 | Extract Image,Table,Text from Documents #rag #multimodal 41:28
- Realtime Multimodal RAG Usecase Part 2 | MultiModal Summrizer | RAG Application #rag #multimodal #ai 29:42
- Realtime Multimodal RAG Usecase Part 3 | MultiVectorRetriever with Langchain | RAG Application #rag 21:44
- Realtime Multimodal RAG Usecase with Google Gemini-Pro-Vision and Langchain | RAG Application #rag 40:30
- End to End RAG App with Hugging face Google Gemma &  MongoDB Vector Search #rag #ai #llm #genai 53:23
- Building Real-Time RAG Pipeline With Mongodb and Pinecone Part-1 #rag #llm #mongodb #pinecone 44:38
- Building Real-Time RAG Pipeline With Mongodb and Pinecone Part-2 #rag #llm #mongodb #pinecone 24:46
- Chat With Multiple Documents(pdfs, docs, txt, pptx etc.) using AstraDB and Langchain #rag #ai 31:43
- Built Powerful Multimodal RAG using Vertex AI(GCP), AstraDb and Langchain #rag #ai 37:45
- End to end E-Commerce Chatbot With AWS Deployment using Astra dB(Cassandra), Langchain & Open AI #ai 01:08:18
- Realtime Powerful RAG Pipeline using Neo4j(Knowledge Graph Db) and Langchain #rag 53:17
- Advance RAG 01 - Powerful RAG Using Hybrid Search(Keyword+vVector search) | Ensemble Retrieval 01:01:10
- Advacne RAG 02 - Hybrid Search (Keyword + Vector ) & Reranking With Cohere API | Ensemble Retrieval 48:16
- End-to-End Weather Chatbot with Google DialogFlow and AWS CI/CD Deployment 02:35:31
- Advanced RAG 03 - Reranking with Sentence Transformers and BM25 API 28:43
- Advanced RAG 04 - Reranking with Cross Encoders, and Cohere API 19:21
- Advance RAG 05 - Merger Retriever and LongContextReorder | Lost in Middle Phenomenon 40:11
- Advance RAG 06- RAG Fusion (Get More Relevant Results for Your RAG) | Reranking With RRF 48:59
- Advance RAG 07 - Flash Reranker for Superfast Reranking 29:48
- Advance RAG 08- Powerful RAG with Langchain Contextual Compression Retriever #ai #llm #openai 57:20
- Advance RAG 09- Powerful RAG with Self Querying Retriever #ai #llm #openai 45:11
- Advance RAG 10- Powerful RAG with Parent Document Retriever #ai #llm #openai #gemini 51:05
- End-to-End RAG With Llama 3.1, Langchain, FAISS and OLlama #ai #llm #llama #huggingface 38:04
- Advance RAG 11- Powerful RAG with Sentence Window Retriever using @LlamaIndex and @qdrant #ai #llm 39:47
- Advance RAG 12- Powerful RAG with Merger Retriever and Hypothetical Document Embeddings(HyDE) #ai 31:53
- Complete @LangChain Essential in 1 shot | LangChain Core | LangServe | LangGraph | LangSmith | Agent 01:55:49
- Chatbot Using @LangChain With Memory(Chat History) | LangChain Core | LangSmith 01:07:50
- RAG Based Chatbot With Memory(Chat History) | Creating History Aware Retriever | Langchain #ai #rag 37:27
- Langchain Conversation Buffer Memory vs Conversation Buffer Window Memory | Chat History#ai #llm #yt 40:45
- Langchain Conversation Entity Memory | Langchain Memory Class | Chat History#ai #llm #yt #chatbot 33:00
- Langchain Conversation Summary Memory vs Conversation Summary Buffer Memory | Chatbot #ai #llm #rag 44:12
- LangChain Expression language(LCEL) for Chaining the Components | All Runnables | Async & Streaming 52:49
- LangGraph 01: Syllabus Introduction of End to End LangGraph Course | LangChain #ai #genai #llm 10:35
- LangGraph:02 LangGraph Course Pre-requist | AI Assistant | RAG I LCEL | Tool & Agent #ai #genai #llm 42:42
- LangGraph:03 LangChain AI Agents | Tools | Tool Calling Agent | ReAct Agents #genai #llm #aiagent 39:15
- LangGraph:04 LangChain ReAct Agent with Custom Tool and Self-Ask Agent with Search | AI Agents #llm 52:15
- LangGraph:05 Building AI Agent from Scratch Using Python with Custom Tool #llm #genai #ai #aiagents 45:31
- LangGraph:06 Detailed Introduction of LangGraph #llm #genai #ai #aiagents 19:49
- LangGraph:07 Code LangGraph From Scratch | LangGraph Deep Dive #llm #genai #ai #aiagents #langchain 27:10
- LangGraph:08 Adding RAG to LangGraph Workflow | LangGraph Deep Dive #llm #genai #aiagents #langchain 37:50
- LangGraph:09 End to End Chatbot using LangGraph With Memory #llm #genai #aiagents #langchain #ai 46:04
- LangGraph:10 Structured Output with LangGraph Agents #llm #genai #aiagents #langchain #ai 26:19
- LangGraph:11 Building Finance Bot with LangGraph's ReAct Agent #llm #genai #aiagents #langchain #ai 30:54
- LangGraph:12 LangGraph Agent with Human-In-The-Loop, Checkpoints & Breakpoints #llm #genai #aiagents 44:22
- LangGraph:13 Corrective RAG for Real Time AI Application #llm #genai #aiagents #ai #langchain #genai 42:36
- LangGraph:14 Agentic RAG for Real Time Agentic AI Application #llm #genai #aiagents #ai #genai 01:06:11
- LangGraph:15 Self-RAG for Real Time Agentic AI Application #llm #genai #aiagents #ai #genai 59:34
- Roadmap of Agentic AI & Generative AI with 150 + Interview Questions and Answers #ai #genai #llm 17:36
- LangGraph:16 Advance SQL Database Agent Powered by LangGraph #llm #genai #aiagents #ai #genai 37:08
- đ„ Autogen Research Agent: End-to-End Project for Paper Analysis & Summarization 01:35:30
- LangGraph:17 Introduction to Multi-Agent System #llm #genai #aiagents #ai #genai #agent 01:07:11
- LangGraph:18 Network or Collaborative Multi-Agent System Implementation #aiagents #ai #genai #agent 52:38
- LangGraph:19 Research and Analysis with Collaborative Multi-Agent System #aiagents #ai #genai #agent 36:44
- LangGraph:20 Supervisor Multi-Agentic System | Agentic AI #aiagents #ai #genai #agent #generativeai 45:54
- LangGraph:21 End-to-End Supervisor Multi-Agentic AI Project for Booking Doctors Appointment #aiagent 01:09:20
- LLM Fine-Tuning: 01 LLM Fine-Tuning From ScratchâFull Playlist Coming Your Way #aiagents #finetuning 20:01
- LLM Fine-Tuning: 02 Understanding Model Pretraining and Training in AI #aiagents #finetuning #ai 01:04:41
- đ„ Live Q&A on Generative & Agentic AIâAsk Me Anything! 37:46
- LLM Fine-Tuning 03: Transfer Learning and Model Fine-Tuning #aiagents #finetuning #ai 01:11:35
- LLM Fine-Tuning 04: Top 10 LLM Fine-Tuning Frameworks for 2025 | Best Tools for Finetuning AI Agents 47:58
- LLM Fine-Tuning 05: Fine-Tuning vs. RAG vs. AI Agents â Which Approach Fits Your Use Case? 27:46
- LLM Fine-Tuning 06: Why Finetuning Was Difficult in RNN or LSTM â How Transformers Changed the Game 50:34
- Advance RAG Course: Master All RAG Retrieval & Reranking Techniques in One VideođĄ! 08:05:39
- LLM Fine-Tuning 07: LSTM vs Transformer | Why Transformers Replaced LSTM in NLP 48:36
- LLM Fine-Tuning 08: Master Hugging Face in 3 Hours | Full Crash Course 2025 #ai #huggingface #llm 03:10:34
- LLM Fine-Tuning 09: Fine-Tuning BERT for NLP (NER, Sentiment, QA) | Hugging Face #huggingface #llm 58:20
- LLM Fine-Tuning 10: LLM Knowledge Distillation | How to Distill LLMs (DistilBERT & Beyond) Part 1 01:03:40
- LLM Fine-Tuning 11: LLM Knowledge Distillation | How to Distill LLMs (LLAMA, Phi & Beyond) Part 2 01:12:42
- LLM Fine-Tuning 12: LLM Quantization Explained( PART 1) | PTQ, QAT, GPTQ, AWQ, GGUF, GGML, llama.cpp 02:12:21
- LLM Fine-Tuning 13: LLM Quantization Explained (PART 2) | PTQ, QAT, GPTQ, AWQ, GGUF, GGML, llama.cpp 03:21:13
- LLMOPS 01: End-to-End Advanced RAG Project with LLMOPS | Complete Setup & Use Cases Discussion 24:00
- LLMOPS 02: Build Multi-Doc Chat with Advanced RAG Part-1| RAG in a Modular Manner (Logger, Config) 01:22:09
- LLMOPS 02: RAG Analysis & Evaluation Strategy Part-2 | Advanced RAG Pipeline in LLMOPS 53:56
- LLMOPS 03: Building API with FastAPI & Swagger Testing | API Development in LLMOPS Project 43:48
- LLMOPS 04: Building UI & Testing the Full App | Frontend Integration in LLMOPS Project 14:04
- LLMOPS 05: Unit & Integration Testing with Pytest | Hands-on Testing in LLMOPS Project 32:45
- LLMOPS 06: CI/CD Deployment with AWS ECS & Fargate | End-to-End GenAI Project Deployment 01:15:47
- Guardrails for LLM Applications | Complete Tutorial for AI Developers WIth Guardrails AI 01:26:12
- LLMOPS 07: Jenkins with Docker | Full CI/CD Pipeline Setup | End-to-End GenAI Project Deployment 35:09
- LLM Fine-Tuning 14: Train LLMs on Your PDF/Text Data | Domain-Specific Fine-Tuning with Hugging Face 01:44:42
- LLM Fine-Tuning 15: Instruction Fine-Tuning Explained | Domain-Specific FineTuning with Hugging Face 55:52
- LLMOPS 09: CI/CD Deployment for LLMOps using GitHub Action on AWS EKS | Deploy LLMOPS Project 57:41
- LLM Fine-Tuning 16: Preference Alignment & Preference Training in LLMs with RLHF, RLAIF, DPO, LoRA 59:38
- LLM Fine-Tuning Crash Course: Finetune model on PDFs, Instruction FT, Preference Training (DPO/RLHF) 03:36:14
- đ„ 100 + Generative AI Interview Questions and Answers Discussion 01:17:41
- LLMOPS 08: CI/CD Deployment on Azure Using Jenkins | Deploy LLMOPS Project Step-by-Step 53:48
- LLM Fine-Tuning 17: Fine-Tune ANY LLM with LLaMA Factory | Full Guide (WebUI + CLI | LoRA + QLoRA) 01:03:20
- LLM Fine-Tuning 18: Unsloth Full Guide | Fine-Tune LLMs 2Ă to 4x Faster with Lowest GPU Memory 55:31
- Generative AI Roadmap 2026 | Complete 3â5 Year AI Career Plan(Beginner to Pro) 01:48:15
- LLM Fine-Tuning 19: Fine-Tune Any LLM with Axolotl đ„ Low-Code YAML Based Training (No Heavy Coding) 57:19
- LLM Fine-Tuning 20: OpenAI(GPTs) Fine-Tuning Masterclass | Supervised FT | Token & Cost Analysis 01:06:18
- LLM Fine-Tuning 21: Google Gemini Fine-Tuning Masterclass using Vertex AI | Supervised Finetuning 58:04
- LLM Fine-Tuning 22: Fine-Tune Any SLM (Small Language Model) | Crash Course with Practical(Unsloth) 53:29
- LLM Fine-Tuning 23: Multimodal LLM Fine-Tuning with Unsloth (Vision + Text) | QwenVL, LLaVA, Pixtral 01:25:58
- LLM Fine-Tuning 24: Embedding & Embedding Fine-Tuning Full Guide | Train Your Own Embedding Model 01:09:49
- LLM Fine-Tuning 25: Improve RAG Retrieval with Finetune Embedding | Embedding Fine-Tuning Full Guide 01:01:49
- Agentic AI Roadmap for AI Engineers | Generative AI â Autonomous AI Systems (2026 Guide) 57:04
- Vibe Engineering (Coding) Crash Course | Build AI Customer Support Agent with AWS Deployment 02:56:07
- Build an AI Customer Support Email Agent using Claude Code | Agentic AI Project 02:03:43
- LoRA & QLoRA Explained Simply | Full Fine-Tuning vs PEFT + Intuition + Practical (Complete Guide) 01:26:39
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