Master the skills for the agentic AI era

Production-level Python for AI engineering. Data structures, APIs, JSON, and error handling.

Master version control and build your portfolio. Repositories, branches, pull requests, and READMEs.

Understand ML vocabulary: models, embeddings, training vs inference. Essential for AI engineers.

Master the art of crafting effective prompts for LLMs, from basics to advanced techniques.

Build AI agents with LangChain, ReAct patterns, and modern orchestration.

Work fluently with OpenAI, Anthropic, and Hugging Face APIs. Streaming, tokens, and error handling.

Run and optimize local models with Ollama, vLLM, and Hugging Face.

Build cohesive LLM pipelines with chains, memory, tools, and LCEL.

Build retrieval-augmented generation systems with vector databases and semantic search.

Enable LLMs to use tools, call APIs, and interact with external systems.

Connect AI models to tools and services safely with the MCP standard.

Build evaluation frameworks, benchmarks, and testing pipelines for AI systems.

Reduce AI costs with caching, model selection, batching, and efficient inference.