Learn skills that get you hired

Go beyond prompting and RAG: fine-tune models with LoRA/QLoRA, deploy and monitor LLM applications in production (LLMOps), and apply responsible AI safety and governance practices.

Build AI agents that use tools, plan multi-step tasks, and remember context, using LangChain and LlamaIndex — then coordinate multiple agents working together on a shared goal.

Learn embeddings and vector databases (Pinecone, Chroma, pgvector), then use them to build Retrieval-Augmented Generation pipelines — chunking, retrieval strategies, hybrid search, and evaluation.

Integrate OpenAI, Anthropic, and Gemini APIs into your own applications — streaming responses, function/tool calling, and managing cost and token usage.

Learn the Python fundamentals and libraries you need before building AI applications: syntax, data structures, virtual environments, and calling external APIs.

A hands-on tour of everyday AI tools — ChatGPT, Claude, Gemini, Midjourney, and Copilot — for boosting productivity without writing code.

Learn practical prompting techniques — few-shot examples, chain-of-thought, system prompts, and structured outputs — to reliably get the results you want from any LLM.

Understand what generative AI and large language models are, how they work at a conceptual level, where they're useful, and where they fail (hallucination, bias, limitations). No coding required.