Grow your AI career with foundational specializations and skill-specific short courses taught by leaders in the field. AI Prompting for Everyone AI Prompting for Everyone Become an AI power user in this new course taught by Andrew Ng. From finding information to building apps, you'll develop the prompting skills that get real, useful results from today's most powerful AI models. Build with Andrew Build with Andrew 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. In this course taught by Andrew Ng, you'll build agentic AI systems that take action through iterative, multi-step workflows. Choose Your Plan Evaluating AI Agents Learn how to systematically evaluate, improve, and iterate on AI agents using structured assessments. Advanced Retrieval for AI with Chroma Learn advanced retrieval techniques to improve the relevancy of retrieved results. Learn to recognize poor query results and use LLMs to improve queries. Safe and reliable AI via guardrails Move your LLM-powered applications beyond proof-of-concept and into production with the added control of guardrails. ChatGPT Prompt Engineering for Developers Learn the fundamentals of prompt engineering for ChatGPT. Learn effective prompting, and how to use LLMs for summarizing, inferring, transforming, and expanding. MCP: Build Rich-Context AI Apps with Anthropic Build AI apps that access tools, data, and prompts using the Model Context Protocol. Generative AI for Everyoneㅤ Learn how to use generative AI's capabilities & limitations. Get an overview of real-world examples, and impact on business & society for effective strategies. AI for Everyone Learn about AI technologies and how to use them. Examine AI's societal impact, and learn to navigate this technological shift. Claude Code: A Highly Agentic Coding Assistant Explore, build, and refine codebases with Claude Code. Orchestrating Workflows for GenAI Applications Turn your GenAI prototype into an automated pipeline using Apache Airflow In this course taught by Andrew Ng, you'll build agentic AI systems that take action through iterative, multi-step workflows. Generative AI for Everyoneㅤ Generative AI for Everyoneㅤ Learn how to use generative AI's capabilities & limitations. Get an overview of real-world examples, and impact on business & society for effective strategies. Machine Learning Machine Learning Specialization Machine Learning Specialization Learn foundational AI concepts through an intuitive visual approach, then learn the code needed to implement the algorithms and math for ML. Deep Learning Specialization Deep Learning Specialization Build neural networks (CNNs, RNNs, LSTMs, Transformers) and apply them to speech recognition, NLP, and more using Python and TensorFlow. Mathematics for Machine Learning Mathematics for Machine Learning and Data Science Mathematics for Machine Learning and Data Science Explore the fundamental mathematics toolkit of machine learning: calculus, linear algebra, statistics, and probability. Claude Code: A Highly Agentic Coding Assistant Claude Code: A Highly Agentic Coding Assistant Explore, build, and refine codebases with Claude Code. Build a solid data analytics foundation using industry standard and AI tools to extract insights, make decisions, and solve real-world business problems. Retrieval Augmented Generation (RAG) Retrieval Augmented Generation (RAG) Gain fundamental understanding and the practical knowledge to develop production-ready RAG applications, from architecture to deployment and evaluation. Python Fundamentals AI Python for Beginners AI Python for Beginners Learn Python programming with AI assistance. Gain skills writing, testing, and debugging code efficiently, and create real-world AI applications. Prototype with AI Fast Prototyping of GenAI Apps with Streamlit Fast Prototyping of GenAI Apps with Streamlit Prototype and deploy GenAI apps using an MVP workflow, prompt engineering, and RAG. Prompt Engineering AI Prompting for Everyone Become an AI power user in this new course taught by Andrew Ng. From finding information to building apps, you'll develop the prompting skills that get real, useful results from today's most powerful AI models. Generative AI for Software Development Learn practical prompt engineering and pair programming techniques with LLMs to write, test, and improve your code. Generative AI with Large Language Models Understand the generative AI lifecycle. Describe transformer architecture powering LLMs. Apply training/tuning/inference methods. Hear from researchers on generative AI challenges/opportunities. Build AI Apps with MCP Server: Working with Box Files 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. Claude Code: A Highly Agentic Coding Assistant Explore, build, and refine codebases with Claude Code. Pydantic for LLM Workflows Build reliable LLM applications with structured outputs and validated data using Pydantic. Transformers in Practice Go beyond just using LLMs to actually understanding them. In this course taught by Sharon Zhou, you'll build the intuition to reason about model behavior, debug real problems, and make smarter decisions about deploying transformer-based models. Generative AI with Large Language Models Understand the generative AI lifecycle. Describe transformer architecture powering LLMs. Apply training/tuning/inference methods. Hear from researchers on generative AI challenges/opportunities. Pydantic for LLM Workflows Build reliable LLM applications with structured outputs and validated data using Pydantic. Post-training of LLMs Adapt LLMs for specific tasks and behaviors using post-training techniques like SFT, DPO, and online RL. Deep Learning Specialization Build neural networks (CNNs, RNNs, LSTMs, Transformers) and apply them to speech recognition, NLP, and more using Python and TensorFlow. Reinforcement Fine-Tuning LLMs With GRPO Improve LLM reasoning with reinforcement fine-tuning and reward functions. Document Processing Document AI: From OCR to Agentic Doc Extraction Build agentic systems to parse documents and extract information grounded in visual components like charts, tables, and forms. Generative AI for Software Development Learn practical prompt engineering and pair programming techniques with LLMs to write, test, and improve your code. Retrieval Augmented Generation (RAG) Gain fundamental understanding and the practical knowledge to develop production-ready RAG applications, from architecture to deployment and evaluation. Build AI Apps with MCP Server: Working with Box Files 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. Agentic Knowledge Graph Construction Build a multi-agent system that plans, designs, and constructs a knowledge graph. Event-Driven Agentic Document Workflows Build an event-driven agentic workflow to process documents and fill forms using RAG and human-in-the-loop feedback. Knowledge Graphs for AI Agent API Discovery Construct a knowledge graph and use it to enable your AI agent to find and call the right APIs in the right order. Agentic Knowledge Graph Construction Build a multi-agent system that plans, designs, and constructs a knowledge graph. Orchestrating Workflows for GenAI Applications Turn your GenAI prototype into an automated pipeline using Apache Airflow Long-Term Agentic Memory With LangGraph Learn to build AI agents with long-term memory with LangGraph, using LangMem for memory management. Event-Driven Agentic Document Workflows Build an event-driven agentic workflow to process documents and fill forms using RAG and human-in-the-loop feedback. Attention in Transformers: Concepts and Code in PyTorch Understand and implement the attention mechanism, a key element of transformer-based LLMs, using PyTorch. Building Adaptive AI Agents AI agents often repeat the same mistakes because they carry nothing forward between sessions. This course teaches three ways to fix that. Learners build a pipeline that turns agent traces into reusable, human-approved skills, build a code knowledge graph that improves retrieval in large codebases, and learn when to adapt the model itself. The result is agents that improve with every run. AI Coding Workflows: From Cloud to Local Learn to take control of your AI coding workflow. Starting from a Claude Code baseline, you'll structure work for smaller models, switch coding agents, connect to different models and providers, and progress from cloud to hybrid and fully local workflows. Along the way, you'll evaluate tradeoffs in cost, speed, and usage metrics. AI can write more code than any team can review by hand. In this hands-on course with Qodo, you'll learn how to make AI code review effective, from running reviews early to giving the reviewer the right context. Then you'll build your own review agent and see why context is what makes a review reliable. Fast LLM Inference with Cerebras This course, built in partnership with Cerebras and taught by Zhenwei Gao, Sebastian Duerr, and Sarah Chieng of Cerebras, shows you how to build LLM applications that respond in real time on Cerebras' Wafer-Scale Engine (WSE-3). It's a chip large enough to hold a model's weights on-chip, right next to the compute units. You'll run fast inference and see where that speed matters most: latency-sensitive use cases like live personalization and real-time multi-tool workflows. Voice for AI Agents and Applications Add voice to your AI agents and applications using three integration patterns: embedded voice, voice layered on existing agents, and voice as a callable tool. Fast & Efficient LLM Inference with vLLM Optimize, deploy, and benchmark an open-source LLM with vLLM.

AI Agents for Image and Video Generation Build AI agents that generate images and videos, evaluate output automatically, and iterate until results meet your quality standards Transformers in Practice Go beyond just using LLMs to actually understanding them. In this course taught by Sharon Zhou, you'll build the intuition to reason about model behavior, debug real problems, and make smarter decisions about deploying transformer-based models. Build Interactive Agents with Generative UI Build fullstack agent apps that go beyond plain text, generating custom UIs like charts, forms, and whiteboards on demand. Vector Databases Knowledge Graphs for AI Agent API Discovery Construct a knowledge graph and use it to enable your AI agent to find and call the right APIs in the right order. Agentic Knowledge Graph Construction Build a multi-agent system that plans, designs, and constructs a knowledge graph. Orchestrating Workflows for GenAI Applications Turn your GenAI prototype into an automated pipeline using Apache Airflow Long-Term Agentic Memory With LangGraph Learn to build AI agents with long-term memory with LangGraph, using LangMem for memory management. Event-Driven Agentic Document Workflows Build an event-driven agentic workflow to process documents and fill forms using RAG and human-in-the-loop feedback. Embedding Models: from Architecture to Implementation Learn how to build embedding models and how to create effective semantic retrieval systems. Generative AI for Software Development Learn practical prompt engineering and pair programming techniques with LLMs to write, test, and improve your code. Claude Code: A Highly Agentic Coding Assistant Explore, build, and refine codebases with Claude Code. Building with Llama 4 Build multimodal and long-context GenAI applications using Llama 4 open models, API, and Llama tools. MCP: Build Rich-Context AI Apps with Anthropic Build AI apps that access tools, data, and prompts using the Model Context Protocol. Long-Term Agentic Memory With LangGraph Learn to build AI agents with long-term memory with LangGraph, using LangMem for memory management. Building toward Computer Use with Anthropic Learn how an AI Assistant is built to use and accomplish tasks on computers. GenAI Applications Fast LLM Inference with Cerebras This course, built in partnership with Cerebras and taught by Zhenwei Gao, Sebastian Duerr, and Sarah Chieng of Cerebras, shows you how to build LLM applications that respond in real time on Cerebras' Wafer-Scale Engine (WSE-3). It's a chip large enough to hold a model's weights on-chip, right next to the compute units. You'll run fast inference and see where that speed matters most: latency-sensitive use cases like live personalization and real-time multi-tool workflows. Voice for AI Agents and Applications Add voice to your AI agents and applications using three integration patterns: embedded voice, voice layered on existing agents, and voice as a callable tool. Build and Train an LLM with JAX Build and train a 20M-parameter LLM from scratch using JAX, the open-source library behind Google's Gemini, and learn the core techniques powering modern AI development. Generative AI for Software Development Learn practical prompt engineering and pair programming techniques with LLMs to write, test, and improve your code. Generative AI with Large Language Models Understand the generative AI lifecycle. Describe transformer architecture powering LLMs. Apply training/tuning/inference methods. Hear from researchers on generative AI challenges/opportunities. Build AI Apps with MCP Server: Working with Box Files 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. AI Coding Workflows: From Cloud to Local Learn to take control of your AI coding workflow. Starting from a Claude Code baseline, you'll structure work for smaller models, switch coding agents, connect to different models and providers, and progress from cloud to hybrid and fully local workflows. Along the way, you'll evaluate tradeoffs in cost, speed, and usage metrics. Fast LLM Inference with Cerebras This course, built in partnership with Cerebras and taught by Zhenwei Gao, Sebastian Duerr, and Sarah Chieng of Cerebras, shows you how to build LLM applications that respond in real time on Cerebras' Wafer-Scale Engine (WSE-3). It's a chip large enough to hold a model's weights on-chip, right next to the compute units. You'll run fast inference and see where that speed matters most: latency-sensitive use cases like live personalization and real-time multi-tool workflows. Spec-Driven Development with Coding Agents Move beyond vibe coding: write clear specs that give your coding agent the context it needs to build intentional, maintainable software. Build with Andrew 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. Multi-vector Image Retrieval 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. Generative AI for Software Development Learn practical prompt engineering and pair programming techniques with LLMs to write, test, and improve your code. Retrieval Augmented Generation (RAG) Gain fundamental understanding and the practical knowledge to develop production-ready RAG applications, from architecture to deployment and evaluation. Agentic Knowledge Graph Construction Build a multi-agent system that plans, designs, and constructs a knowledge graph. Claude Code: A Highly Agentic Coding Assistant Explore, build, and refine codebases with Claude Code. Orchestrating Workflows for GenAI Applications Turn your GenAI prototype into an automated pipeline using Apache Airflow DSPy: Build and Optimize Agentic Apps Build, debug, and optimize AI agents using DSPy and MLflow. Long-Term Agentic Memory With LangGraph Learn to build AI agents with long-term memory with LangGraph, using LangMem for memory management. Fine-tuning & RL for LLMs: Intro to Post-training Learn how to apply fine-tuning and reinforcement learning techniques to shape model behavior, improve reasoning, and make LLMs safer and more reliable. Generative AI with Large Language Models Understand the generative AI lifecycle. Describe transformer architecture powering LLMs. Apply training/tuning/inference methods. Hear from researchers on generative AI challenges/opportunities. Pydantic for LLM Workflows Build reliable LLM applications with structured outputs and validated data using Pydantic. Post-training of LLMs Adapt LLMs for specific tasks and behaviors using post-training techniques like SFT, DPO, and online RL. Reinforcement Fine-Tuning LLMs With GRPO Improve LLM reasoning with reinforcement fine-tuning and reward functions. Building AI Browser Agents Build agents that navigate and interact with websites, and learn how to make them more reliable. Evaluation and Monitoring Agentic Knowledge Graph Construction Build a multi-agent system that plans, designs, and constructs a knowledge graph. Claude Code: A Highly Agentic Coding Assistant Explore, build, and refine codebases with Claude Code. Pydantic for LLM Workflows Build reliable LLM applications with structured outputs and validated data using Pydantic. Post-training of LLMs Adapt LLMs for specific tasks and behaviors using post-training techniques like SFT, DPO, and online RL. Orchestrating Workflows for GenAI Applications Turn your GenAI prototype into an automated pipeline using Apache Airflow DSPy: Build and Optimize Agentic Apps Build, debug, and optimize AI agents using DSPy and MLflow. Search and Retrieval Multi-vector Image Retrieval 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 AI Apps with MCP Server: Working with Box Files 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. Knowledge Graphs for AI Agent API Discovery Construct a knowledge graph and use it to enable your AI agent to find and call the right APIs in the right order. Agentic Knowledge Graph Construction Build a multi-agent system that plans, designs, and constructs a knowledge graph. DSPy: Build and Optimize Agentic Apps Build, debug, and optimize AI agents using DSPy and MLflow. Long-Term Agentic Memory With LangGraph Learn to build AI agents with long-term memory with LangGraph, using LangMem for memory management. Building with Llama 4 Build multimodal and long-context GenAI applications using Llama 4 open models, API, and Llama tools. Building toward Computer Use with Anthropic Learn how an AI Assistant is built to use and accomplish tasks on computers. Reasoning with o1 Learn how to use and prompt OpenAI's o1 model for complex reasoning tasks. Collaborative Writing and Coding with OpenAI Canvas Learn to use OpenAI Canvas to write, code, and create more effectively in collaboration with AI. Introducing Multimodal Llama 3.2 Try out the features of the new Llama 3.2 models to build AI applications with multimodality. Building Multimodal Search and RAG Build smarter search and RAG applications for multimodal retrieval and generation.