
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.
Build agents that communicate and collaborate across different frameworks using ACP.
Build, debug, and optimize AI agents using DSPy and MLflow.
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.
Build an interactive system for querying video content using multimodal AI
Learn how to build embedding models and how to create effective semantic retrieval systems.
Optimize the efficiency, security, query processing speed, and cost of your RAG applications.
Interact with tabular data and SQL databases using natural language, enabling more efficient and accessible data analysis.
Build agentic AI workflows using LangChain's LangGraph and Tavily's agentic search.
Build smarter search and RAG applications for multimodal retrieval and generation.
Build autonomous agents that intelligently navigate and analyze your data. Learn to develop agentic RAG systems using LlamaIndex, enabling powerful document Q&A and summarization. Gain valuable skills in guiding agent reasoning and debugging.
Build a full-stack web application that uses RAG capabilities to chat with your data. Learn to build a RAG application in JavaScript, using an intelligent agent to answer queries.
Learn how to build and use knowledge graph systems to improve your retrieval augmented generation applications. Use Neo4j's query language Cypher to manage and retrieve data.
Expand your toolkit with LangChain.js, a JavaScript framework for building with LLMs. Understand the fundamentals of using LangChain to orchestrate and chain modules.
Learn advanced retrieval techniques to improve the relevancy of retrieved results. Learn to recognize poor query results and use LLMs to improve queries.
Learn advanced RAG retrieval methods like sentence-window and auto-merging that outperform baselines, and evaluate and iterate on your pipeline's performance.
Design and execute real-world applications of vector databases. Build efficient, practical applications, including hybrid and multilingual searches.
Learn how to accelerate the application development process with text embeddings for sentence and paragraph meaning.
Learn to use LLMs to enhance search and summarize results, using Cohere Rerank and embeddings for dense retrieval.