From zero to professional level: learn the keys to Generative AI, LLM Apps, AI Agents, and Cursor AI.
What you'll learn
- Keys to AI, Generative AI, LLM Apps, and new AI Coding Assistants like Cursor AI.
- LLM Apps with LangChain, CrewAI, LangGraph, LangServe and LangSmith.
- How to build apps without coding using Cursor AI and AI Coding Assistants.
- How to build the new Multimodal and Multi-Agent LLM Applications.
- Opportunities and threats of AI for businesses, startups, and jobs.
- RAG Applications in Depth: Full Stack RAG Apps and Advanced Techniques.
- How to manage LLMOps: Observability, Evaluation, Testing, Etc.
- Professional opportunities opened by Artificial Intelligence.
- Steps to become an Artificial Intelligence Engineer.
- How to introduce Artificial Intelligence into your business.
- Keys to LLM Applications, the highest potential applications of Generative AI.
- Architecture of professional LLM Applications.
- The RAG Technique (Retrieval Augmented Generation).
- Artificial Intelligence Agents.
- Basic and advanced LangChain, LangChain LCEL, and LangChain v010. LangSmith, LangServe, LangChain Templates.
- LCEL (LangChain Expression Language) in depth.
- Basic and advanced LlamaIndex. LlamaIndex Templates.
- ChatGPT, OpenAI, OpenAI functions, and the OpenAI API.
- Large Language Models (LLM): ChatGPT, Llama2, Mistral, Falcon, etc.
- Vector databases: Postgres, Pinecone, Chroma, FAISS, DeepLake, etc.
- Full-Stack Applications: Nextjs and FastAPI.
- Professional deployment: Vercel and Render.
- Provisional deployment: Streamlit.
- Cloud hosting: AWS S3.
- How to apply the principles of Responsible AI.
- Daily tools of the AI Engineer: Jupyter Notebooks, Python, Terminal, Github, Codespaces, etc.