Generative AI for complete beginners (2026)Published 7/2026
MP4 |
Video: h264, 1920x1080 |
Audio: AAC, 44.1 KHz, 2 Ch
Language: English |
Duration: 7h 42m |
Size: 5.94 GB
Generative AI and concepts behind ChatGpt, Claude and other AI tools
What you'll learnIntroduction to Generative AI, difference between GenAI and Traditional AI & local LLMs set up using Ollama
Ollama installation
Foundation Models including OpenAI, Llama, Gemini, Claude & Qwen, and Transformer Architecture
Prompt Engineering including zero shot, few shot & chain of thought, different prompts use for different tasks
Why Vector Databases, differences between Vector Databases including Pinecone, Chroma & FAISS and traditional DBs.
Frameworks including LangChain, LlamaIndex & vLLM
Model Compression & Finetuning including Lora, QLora & PEFT. Finetuning using GCP and LORA
Generative AI usage and Architectures (IR, RAG, RAG with reRanker, Hybrid Search, Contextual RAG & LightRAG) & Observability
Applications & APIs using Streamlit & Gradio. Exposing LLMs using REST end points. Natural language to SQL generation. Agents & Multi Agent Systems.
RequirementsBasic programming skills
DescriptionTopics covered in this course.
1. Introduction to Generative AI, difference between GenAI and Traditional AI, different AI models including Linear Regression, Classification and Clustering & local LLMs set up using Ollama
2. Foundation Models including OpenAI, Llama, Gemini, Claude & Qwen, why parameters matter, different between regular LLms and reasoning LLMs and Transformer Architecture
3. Prompt Engineering including zero shot, few shot & chain of thought, different prompts use for different tasks
4. Why Vector Databases, differences between Vector Databases including Pinecone, Chroma & FAISS and traditional DBs. How we ingest company knowledge base to vector DBs using chunking and embeddings and how we do semantic search.
5. Frameworks including LangChain, LlamaIndex & vLLM. Building different applications using LangChain and LlamaIndex frameworks and how we use vLLM for deploying models to local or on prem environment and use for inferencing.
6. Model Compression & Finetuning including Lora, QLora & PEFT. Finetuning using GCP and LORA. Different quantization techniques.
7. Generative AI usage and Architectures (IR, RAG, RAG with reRanker, Hybrid Search, Contextual RAG & LightRAG) & Observability
8. Applications & APIs using Streamlit & Gradio. Exposing LLMs using REST end points for UI to consume. Natural language to SQL generation. Agents & Multi Agent Systems. Difference between Gen AI and Agentic AI. Different Agentic AI architectures.
Who this course is forGenerative AI beginners
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