AI Courses
Grow your AI career with foundational specializations and skill-specific short courses taught by leaders in the field.
Grow your AI career with foundational specializations and skill-specific short courses taught by leaders in the field.
Build agents that collaborate to solve complex business tasks.
Learn Python programming with AI assistance. Gain skills writing, testing, and debugging code efficiently, and create real-world AI applications.
Learn advanced retrieval techniques to improve the relevancy of retrieved results. Learn to recognize poor query results and use LLMs to improve queries.
Build neural networks (CNNs, RNNs, LSTMs, Transformers) and apply them to speech recognition, NLP, and more using Python and TensorFlow.
Learn how to systematically evaluate, improve, and iterate on AI agents using structured assessments.
Learn how an AI Assistant is built to use and accomplish tasks on computers.
Learn the fundamentals of prompt engineering for ChatGPT. Learn effective prompting, and how to use LLMs for summarizing, inferring, transforming, and expanding.
Build real-world applications from the command line using Gemini CLI, Google's open-source agentic coding assistant that coordinates local tools and cloud services to automate coding and creative workflows.
If you've never written code before, this course is for you. In less than 30 minutes, you'll learn to describe an idea in words and let AI transform it into an app for you.
Build advanced retrieval systems that represent images with multiple vectors, enabling fine-grained matching between text queries and visual content for accurate multi-modal search.
Learn to code with AI in Jupyter notebooks. Use Jupyter AI to generate code, get explanations, and analyze data.
Explore, build, and refine codebases with Claude Code.
Learn practical prompt engineering and pair programming techniques with LLMs to write, test, and improve your code.
Build real-world applications from the command line using Gemini CLI, Google's open-source agentic coding assistant that coordinates local tools and cloud services to automate coding and creative workflows.
Build agentic systems to parse documents and extract information grounded in visual components like charts, tables, and forms.
Build an LLM app that uses tools from the Box MCP server to discover Box files and extract text from them. Transform it into a multi-agent system that communicates using A2A.
Explore, build, and refine codebases with Claude Code.
Learn practical prompt engineering and pair programming techniques with LLMs to write, test, and improve your code.
Build reliable LLM applications with structured outputs and validated data using Pydantic.
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 reliable LLM applications with structured outputs and validated data using Pydantic.
Build neural networks (CNNs, RNNs, LSTMs, Transformers) and apply them to speech recognition, NLP, and more using Python and TensorFlow.
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.
Learn the essential steps to pretrain a large language model from scratch.
Learn how to quantize any open-source model. Learn to compress models with the Hugging Face Transformers library and the Quanto library.
Build a multi-agent system that plans, designs, and constructs a knowledge graph.
Explore, build, and refine codebases with Claude Code.
Gain fundamental understanding and the practical knowledge to develop production-ready RAG applications, from architecture to deployment and evaluation.
Build agents that communicate and collaborate across different frameworks using ACP.
Build, debug, and optimize AI agents using DSPy and MLflow.
Build systems with MemGPT agents that can autonomously manage their memory.
Build an LLM app that uses tools from the Box MCP server to discover Box files and extract text from them. Transform it into a multi-agent system that communicates using A2A.
Build a multi-agent system that plans, designs, and constructs a knowledge graph.
Explore, build, and refine codebases with Claude Code.
Learn practical prompt engineering and pair programming techniques with LLMs to write, test, and improve your code.
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.
Explore, build, and refine codebases with Claude Code.
Learn practical prompt engineering and pair programming techniques with LLMs to write, test, and improve your code.
Build multimodal and long-context GenAI applications using Llama 4 open models, API, and Llama tools.
Build AI apps that access tools, data, and prompts using the Model Context Protocol.
Learn to build AI agents with long-term memory with LangGraph, using LangMem for memory management.
Learn how an AI Assistant is built to use and accomplish tasks on computers.
Build multimodal and long-context GenAI applications using Llama 4 open models, API, and Llama tools.
Learn how an AI Assistant is built to use and accomplish tasks on computers.
Learn how to use and prompt OpenAI's o1 model for complex reasoning tasks.
Learn to use OpenAI Canvas to write, code, and create more effectively in collaboration with AI.
Try out the features of the new Llama 3.2 models to build AI applications with multimodality.
Build smarter search and RAG applications for multimodal retrieval and generation.
Build agentic systems to parse documents and extract information grounded in visual components like charts, tables, and forms.
Build an LLM app that uses tools from the Box MCP server to discover Box files and extract text from them. Transform it into a multi-agent system that communicates using A2A.
Build a multi-agent system that plans, designs, and constructs a knowledge graph.
Learn practical prompt engineering and pair programming techniques with LLMs to write, test, and improve your code.
Gain fundamental understanding and the practical knowledge to develop production-ready RAG applications, from architecture to deployment and evaluation.
Build an event-driven agentic workflow to process documents and fill forms using RAG and human-in-the-loop feedback.
Construct a knowledge graph and use it to enable your AI agent to find and call the right APIs in the right order.
Build a multi-agent system that plans, designs, and constructs a knowledge graph.
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 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.
Build a multi-agent system that plans, designs, and constructs a knowledge graph.
Explore, build, and refine codebases with Claude Code.
Build reliable LLM applications with structured outputs and validated data using Pydantic.
Build, debug, and optimize AI agents using DSPy and MLflow.
Build responsive, scalable, and human-like AI voice applications.
Build agents that write and execute code to perform complex tasks, using Hugging Face’s smolagents.
Construct a knowledge graph and use it to enable your AI agent to find and call the right APIs in the right order.
Build a multi-agent system that plans, designs, and constructs a knowledge graph.
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.
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.
Build reliable LLM applications with structured outputs and validated data using Pydantic.
Build agents that navigate and interact with websites, and learn how to make them more reliable.
Learn how to use generative AI's capabilities & limitations. Get an overview of real-world examples, and impact on business & society for effective strategies.
Try out the features of the new Llama 3.2 models to build AI applications with multimodality.
Systematically improve the accuracy of LLM applications with evaluation, prompting, and memory tuning.
Learn the essential steps to pretrain a large language model from scratch.
Build advanced retrieval systems that represent images with multiple vectors, enabling fine-grained matching between text queries and visual content for accurate multi-modal search.
Build an LLM app that uses tools from the Box MCP server to discover Box files and extract text from them. Transform it into a multi-agent system that communicates using A2A.
Construct a knowledge graph and use it to enable your AI agent to find and call the right APIs in the right order.
Build a multi-agent system that plans, designs, and constructs a knowledge graph.
Build agents that communicate and collaborate across different frameworks using ACP.
Build, debug, and optimize AI agents using DSPy and MLflow.
