DeepLearning.AI
@DeepLearningAI • 346,734 subscribers
We are an education technology company with the mission to grow and connect the global AI community.
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Build coding agents that learn from experience. In Building Adaptive AI Agents, you’ll turn an agent’s own traces into reusable skills and build a code knowledge graph that finds the right context where keyword search misses. Built in partnership with Oracle and taught by Nacho Martínez (wanderer) and Casius Lee. Enroll for free:
DeepLearning.AI21,534 Aufrufe • vor 8 Tagen
AI can write more code than any team can review by hand, and a pull request can look fine while hiding a security issue or missing a requirement. In our new short course, AI Code Review, built in collaboration with Qodo and taught by Nnenna 👩🏽💻✨, you'll learn the practices that make AI code review effective: review before you open a pull request, give the reviewer full context about your codebase, and triage findings by risk. Then you'll build your own review agent, from a context engine that finds the right code to a team of specialized reviewers. Enroll for free:
DeepLearning.AI55,967 Aufrufe • vor 1 Monat
📢 New short course in collaboration with Google: Build and Train an LLM with JAX. In this course, you’ll implement and train a 20M-parameter MiniGPT-style language model from scratch using JAX, the open-source library behind Gemini. You’ll build the model architecture, load and preprocess training data, implement the training loop, save checkpoints, and generate text through a chat interface. Taught by Chris Achard, Developer Relations Engineer on Google’s TPU Software team. Enroll now:
DeepLearning.AI46,065 Aufrufe • vor 6 Monaten
You don't need to know how to code to build apps anymore. Andrew Ng's new course shows how to turn an idea into a working web app simply by describing what you want to AI. You'll build something real in under 30 minutes. "Build with Andrew" is our most beginner-friendly course yet. If you know someone who has ideas but thinks building is too complicated—friends, parents, coworkers—please share this with them. Start building today:
DeepLearning.AI45,879 Aufrufe • vor 7 Monaten
Sharing our latest short course: Building and Evaluating Data Agents, created in collaboration with Snowflake and taught by Anupam Datta (Anupam Datta) and Josh Reini (Josh Reini). A data agent extracts data from sources such as files or databases, analyzes it, and provides insights and visualizes its findings. But most data agents struggle with reliability or can't handle multi-step reasoning. In this course, you'll learn to build, trace, and evaluate a multi-agent workflow that plans tasks, pulls context from structured and unstructured data, performs web search, and summarizes or visualizes the final results. Learn more and enroll for free!
DeepLearning.AI40,867 Aufrufe • vor 11 Monaten
New course added! We’re launching “Knowledge Graphs for AI Agent API Discovery,” built in collaboration with SAP and taught by Pavithra G K and Lars Heling. When agents call APIs in the wrong sequence, entire workflows collapse. The challenge? Most agents lack the contextual understanding of which endpoints depend on others and why order matters in real business processes. This course teaches you to: ✅Construct a knowledge graph from API specifications ✅ Extend it with business-process data so dependencies and order are explicit ✅ Use semantic retrieval and process edges to find required APIs and their sequence, then build an agent to execute them Enroll now!
DeepLearning.AI41,683 Aufrufe • vor 11 Monaten
New course in collaboration with CrewAI, and taught by its Co-Founder and CEO, João Moura! In "Design, Develop, and Deploy Multi-Agent Systems" you'll go deep into building teams of AI agents that collaborate to handle complex, end-to-end workflows. You’ll design systems that plan, reason, and coordinate, with tools, memory, and guardrails that make them reliable and production-ready. Learn more and enroll now 👉 Featuring insights from Weaviate AI Database, Snyk, Exa, and AB InBev, whose work with CrewAI demonstrates how multi-agent systems are being applied in the field today.
DeepLearning.AI33,849 Aufrufe • vor 9 Monaten
Building a machine learning model isn’t just about finding the right algorithm—it’s about the right process. In Machine Learning in Production, Andrew Ng breaks down the iterative loop of model development: training, error analysis, refining hyperparameters, and improving data. Getting to a high test set accuracy is one thing, but aligning your model with real-world business metrics? That’s where the real challenge begins. Learn how to bridge the gap between models and impact:
DeepLearning.AI45,348 Aufrufe • vor 1 Jahr
Vibe coding works… until your agent confidently builds the wrong thing. Write specs first. Keep your agent aligned with what you actually want to build. Enroll in our Spec-Driven Development course to learn a more intentional way to build with coding agents:
DeepLearning.AI14,291 Aufrufe • vor 3 Monaten
Our course recommendation of the day is “Post-training of LLMs, ” where you’ll learn how to customize pre-trained language models using Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Online Reinforcement Learning (RL). You'll learn when to use each method, how to curate training data, and implement them in code to shape model behavior effectively. Enroll here:
DeepLearning.AI29,369 Aufrufe • vor 11 Monaten
OCR can process characters but it doesn’t understand pixels. OCR has no way to reason about the headers, totals, or checkboxes found in tables, invoices, or forms. In our course with LandingAI, "Document AI: From OCR to Agentic Doc Extraction," we build agents to address these failure modes by breaking documents into pieces, applying the right tools, and mapping information to expected formats. Learn more and enroll today:
DeepLearning.AI21,554 Aufrufe • vor 7 Monaten
Last week, we launched "Attention in Transformers: Concepts and Code in PyTorch" instructed by Joshua Starmer! In this course, you'll: ✅ Learn how the attention mechanism in LLMs helps convert base token embeddings into rich context-aware embeddings. ✅ Understand the Query, Key, and Value matrices, what they are for, how to produce them, and how to use them in attention. ✅ Learn the difference between self-attention, masked self-attention, and cross-attention, and how multi-head attention scales the algorithm. 🔗 Enroll for free:
DeepLearning.AI36,832 Aufrufe • vor 1 Jahr
Production-ready RAG systems need observability. From tracking latency and throughput to evaluating response quality with human feedback or LLM-as-a-judge, robust observability gives you visibility into both system performance and output quality, on both a component and system-wide level. This lesson from our Retrieval Augmented Generation course breaks down the core components of an effective eval system and how to balance cost, automation, and accuracy when choosing your metrics. 📚 Learn more in the full course:
DeepLearning.AI18,793 Aufrufe • vor 7 Monaten
CEOs are wasting millions on AI. Here's why. 💸 CEOs are pouring millions into AI, but simply swapping a human for an AI agent in the middle of a broken process won't change your business. True AI transformation requires an end-to-end workflow redesign. Don't just use AI to make the old way faster—use it to build a completely new experience. Ready to build AI workflows that actually move the needle? DeepLearning.AI offers free short courses to help you master agentic workflows and the latest AI tools. Start learning today: Subscribe to The Batch newsletter for weekly AI insights:
DeepLearning.AI11,551 Aufrufe • vor 5 Monaten
In case you missed it, earlier this week we launched "MCP: Build Rich-Context AI Apps with Anthropic." Enroll now and: ✅ Explore how MCP standardizes access to tools and data for AI applications, its underlying architecture, and how it simplifies the integration of new tools and connections to external systems (e.g., GitHub repos, Google Docs, local files). ✅ Build and deploy an MCP server that provides tools, resources, and prompts, and add it to the configuration of AI applications, such as Claude Desktop, to extend them. ✅ Build an MCP-compatible application that hosts multiple MCP clients, each maintaining 1-to-1 connection to an MCP server. Join in for free:
DeepLearning.AI23,829 Aufrufe • vor 1 Jahr