
AI Python for Beginners
Learn Python programming with AI assistance. Gain skills writing, testing, and debugging code efficiently, and create real-world AI applications.
Grow your AI career with foundational specializations and skill-specific short courses taught by leaders in the field.
Adapt LLMs for specific tasks and behaviors using post-training techniques like SFT, DPO, and online RL.
Build agents that communicate and collaborate across different frameworks using ACP.
Build multimodal and long-context GenAI applications using Llama 4 open models, API, and Llama tools.
Build a solid data analytics foundation using industry standard and AI tools to extract insights, make decisions, and solve real-world business problems.
Turn your GenAI prototype into an automated pipeline using Apache Airflow
Build, debug, and optimize AI agents using DSPy and MLflow.
Improve LLM reasoning with reinforcement fine-tuning and reward functions.
Build AI apps that access tools, data, and prompts using the Model Context Protocol.
Build responsive, scalable, and human-like AI voice applications.
Build systems with MemGPT agents that can autonomously manage their memory.
Build agents that write and execute code to perform complex tasks, using Hugging Face’s smolagents.
Build agents that navigate and interact with websites, and learn how to make them more reliable.
Learn how to generate structured outputs to power production-ready LLM software applications.
Design, build, and deploy apps with an AI coding agent in an integrated web development environment.
Learn to build AI agents with long-term memory with LangGraph, using LangMem for memory management.
Build an event-driven agentic workflow to process documents and fill forms using RAG and human-in-the-loop feedback.
Learn to build, debug, and deploy applications with an Agentic AI-powered integrated development environment.
Learn how to systematically evaluate, improve, and iterate on AI agents using structured assessments.
Understand and implement the attention mechanism, a key element of transformer-based LLMs, using PyTorch.
Understand the transformer architecture that powers LLMs to use them more effectively.