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DeepLearning.AI offers a large catalog of short courses on generative AI, LLM applications, retrieval, agents, model deployment, and multimodal systems. Below is a curated set of 30+ relevant options, organized by what you want to learn—not a claim that every course in the catalog is a generative-AI course. Access terms were checked against the catalog and course information for August 16–18, 2026; free availability can change, so confirm each course page before enrolling.
What “free” means on DeepLearning.AI
Free course access does not necessarily mean every part of a learning experience is free. Learning content, graded assignments, certificates, and external tools can have different terms. DeepLearning.AI separates Short Courses, longer Courses, and Professional Certificates in its learning platform and short-course catalog.
| Access type | What to expect |
|---|---|
| Free learning access | Course content can be accessed without paying, subject to the individual course’s current terms. |
| Limited-time or beta access | Access may be temporary. For example, the page for Orchestrating Workflows for GenAI Applications describes free access as limited-time or beta-dependent. |
| Preview or audit access | Some courses provide only part of the course without payment. Generative AI with Large Language Models, for example, offers a free first-module preview rather than a wholly free short course. |
| Paid learning features | Graded assignments, certificates, or other premium features may require DeepLearning.AI Pro. The membership page describes the paid membership, and the Generative AI for Everyone page associates certificate earning with Pro. |
| External costs | Course enrollment may be free while exercises use paid API calls, cloud compute, GPU resources, or hosted databases. |
Do not assume a free course includes a free certificate, or that its access terms will stay the same. Check the enrollment page and any tool or service you plan to use.
How to choose a course
Use the labels below as practical guidance, not official ratings: Beginner means a reasonable starting point for a newcomer; Intermediate assumes some familiarity with AI tools or software development; Advanced/technical is aimed at people comfortable with coding, model concepts, or infrastructure. The course pages do not establish a duration for every entry, so this guide does not invent one.
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#1 Best Overall
Elebase USB to USB C Adapter for iPhone 18 Pro Max,USBC Car Charger Adapter
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- New to GenAI: Begin with prompting and application concepts before attempting agent frameworks.
- Building an app: Learn model APIs, then retrieval, tool use, and evaluation.
- Working on RAG: Study embeddings and retrieval quality before adding agents or multimodal inputs.
- Serving models: Choose fine-tuning, quantization, and inference material only if you have the relevant Python and ML background.
Beginner foundations and practical GenAI
These are useful entry points, though not all are technically short courses. Full courses are marked as such rather than counted as short-course entries.
- AI Prompting for Everyone — Beginner. A starting point for prompting, information retrieval, transformation, and app-building concepts.
- Build with Andrew — Beginner. A beginner-friendly route to AI-assisted app creation for people who have not written code.
- ChatGPT Prompt Engineering for Developers — Beginner to intermediate. Covers prompting for tasks such as summarizing, inferring, transforming, and expanding text. It is more useful if you are comfortable following developer-oriented examples.
- AI Python for Beginners — Beginner. Python learning with AI assistance; useful preparation for coding-focused courses.
- Generative AI for Everyone — Beginner; full course, not a short course. Introduces what generative AI can and cannot do, with business and societal applications. Its page describes a roughly five-hour course and identifies certificate earning with Pro.
- AI for Everyone — Beginner; full course, not a GenAI short course. Broad AI literacy and business-oriented fundamentals make it useful preparation, but it is not specifically a generative-AI course.
Prompt engineering and LLM applications
These options move from writing useful prompts to building applications that call models and coordinate multiple steps.
Rank #2
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- 5-in-1 USB-C Hub: Experience comprehensive connectivity featuring a Power Delivery input, two USB-A 2.0 ports, a USB-A 3.0 port, and an HDMI port. (Note: The USB-C power delivery input port is only for connecting an external wall charger to power your laptop and cannot power peripheral devices.)
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- What You Get: Anker USB-C Hub (5-in-1, 4K HDMI), welcome guide, 18-month warranty, and our friendly customer service.
- Building Systems with the ChatGPT API — Intermediate. Focuses on constructing multi-step applications using an LLM API. API concepts and some programming familiarity help.
- LangChain for LLM Application Development — Intermediate. Introduces chains, prompts, memory, and application patterns in LangChain.
- LangChain: Chat with Your Data — Intermediate. Applies retrieval techniques to question-answering over documents.
- Prompt Engineering for Vision Models — Intermediate. The listed course URL uses a Llama 2 slug; check the live page for current title and content before enrolling, as course titles and examples can change.
- Large Language Models with Semantic Search — Intermediate. Connects LLM applications with search and semantic retrieval.
- Orchestrating Workflows for GenAI Applications — Intermediate. Covers workflow orchestration for generative-AI applications. Its access terms may be temporary or beta-dependent.
RAG, retrieval, embeddings, and vector databases
Retrieval-augmented generation (RAG) adds retrieved information to a model’s input. A vector database can support semantic retrieval, but it is only one component: data preparation, retrieval quality, evaluation, and handling untrusted content matter too.
- Vector Databases: from Embeddings to Applications — Intermediate. Covers embeddings, vector storage, and application use cases.
- Embedding Models: From Architecture to Implementation — Intermediate to advanced. Explores how embedding models are designed and used.
- Large Language Models with Semantic Search — Intermediate. A practical bridge between semantic search and LLM-powered applications. It also appears above because it serves both application and retrieval goals.
- LangChain: Chat with Your Data — Intermediate. A framework-based introduction to document retrieval and question-answering.
- Advanced Retrieval for AI with Chroma — Advanced/technical. Focuses on advanced retrieval methods using Chroma.
- Retrieval Optimization: From Tokenization to Vector Quantization — Advanced/technical. Examines retrieval quality and efficiency from tokenization through vector quantization.
- Building and Evaluating Advanced RAG Applications — Advanced/technical. Covers advanced RAG design and evaluation.
- Building and Evaluating Advanced RAG — Advanced/technical. A separate catalog entry with a similar title; inspect its live page to confirm the course scope and avoid enrolling in a duplicate of material you already have.
- Knowledge Graphs for RAG — Advanced/technical. Adds structured knowledge to retrieval-augmented applications.
- Building RAG Agents with LLMs — Advanced/technical. Combines retrieval-augmented generation with agent architecture.
- Building Agentic RAG with LlamaIndex — Advanced/technical. Develops agentic retrieval workflows with LlamaIndex.
Agents, tools, and workflows
Agents add actions and multi-step control to model applications. Start with tool use before adding orchestration frameworks or multiple agents; a fluent agent response is not evidence that its actions or conclusions are correct.
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- Sleek 7-in-1 USB-C Hub: Features an HDMI port, two USB-A 3.0 ports, and a USB-C data port, each providing 5Gbps transfer speeds. It also includes a USB-C PD input port for charging up to 100W and dual SD and TF card slots, all in a compact design.
- Flawless 4K@60Hz Video with HDMI: Delivers exceptional clarity and smoothness with its 4K@60Hz HDMI port, making it ideal for high-definition presentations and entertainment. (Note: Only the HDMI port supports video projection; the USB-C port is for data transfer only.)
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- What You Get: Anker USB-C Hub (7-in-1), welcome guide, 18-month warranty, and our friendly customer service.
- Function-Calling and Tool Use with LLMs — Intermediate. Teaches how models can call functions and external tools through structured interactions.
- AI Agents in LangGraph — Intermediate to advanced. Covers stateful agent workflows and graph-based orchestration. It is listed once here to avoid counting the same course twice.
- Multi AI Agent Systems with crewAI — Intermediate to advanced. Introduces coordination among specialized agents.
- Design, Develop, and Deploy Multi-Agent Systems with CrewAI — Intermediate to advanced; catalog course, verify whether it is a short course. The catalog lists a practical CrewAI course covering multi-agent systems; check its course type and current scope before treating it as a short-course offering.
- Building Agentic AI Applications with LlamaIndex — Intermediate to advanced. Applies LlamaIndex components to agentic applications.
- Building Coding Agents with Tool Execution — Advanced/technical. Covers agents that write and execute code in controlled environments.
- Agent Skills with Anthropic — Intermediate to advanced. Explores specialized, on-demand agent capabilities for tasks such as coding, research, and data analysis.
- Agent Memory: Building Memory-Aware Agents — Advanced/technical. Focuses on storing, retrieving, and refining information across agent sessions.
- Build Interactive Agents with Generative UI — Advanced/technical. Covers agents that generate interactive interfaces, such as forms, charts, and whiteboards.
- Agentic AI — Course, not confirmed as a short course. A catalog course taught by Andrew Ng; check its format and access terms rather than counting it as a short course by default.
Fine-tuning, inference, and model operations
These are technical choices for learners who already understand the basics of LLM applications. They are not necessary prerequisites for every GenAI developer.
- Finetuning Large Language Models — Advanced/technical. Explains adapting pretrained models to specialized tasks.
- Quantization Fundamentals with Hugging Face — Advanced/technical. Covers ways to reduce model memory and computation requirements.
- Efficiently Serving LLMs — Advanced/technical. Focuses on techniques for deploying language models more efficiently.
- Fast and Efficient LLM Inference with vLLM — Advanced/technical. Covers optimizing, deploying, and benchmarking open-source LLM inference.
- Efficient Inference with SGLang: Text and Image Generation — Advanced/technical. Covers caching and inference optimization for text and image generation.
- Build and Train an LLM with JAX — Advanced/technical. Builds a small language model while introducing core training techniques; expect a substantially steeper technical path than a prompting course.
Multimodal, image, video, and voice
Multimodal courses work across combinations of text, images, audio, or video. They can require access to model APIs or compute beyond the course material.
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- Next-Level 10Gbps & 3A Charging: No more slow 480Mbps, this usb to usb c adapter has a transfer speed of up to 10Gbps, allowing you to do more transferring in less time. This usb adapter fits both USB A and USB C charger, supporting up to 3A fast charging
- Upgraded Exquisite Craftsmanship: With an aluminum alloy housing and metal connector, the usbc to usb adapter is extremely durable and sturdy. Rigorously tested to withstand more than 10,000 times of plugging and unplugging, ensuring long-lasting performance
- Broad Compatible: The usb c to usb adapter widely supports all USB C/ USB A devices like laptops, tablets, cellphones, car chargers, and phone chargers. Such as compatible with MacBook Pro/Air 2023/2022, Thunderbolt 4/3 Devices,Apple MagSafe Watch 9/8/7/SE/Ultra, iPad Pro 2022/2021, Samsung Galaxy S23/S20/S10, and iPhone 17/16/15 Pro. Plug and play
- Please Note: To reach 10Gbps speed, keep the cable under 3.3 ft. For USB A Male to USB C adapters, try flipping the USB C connector. USB C Male to USB A adapters support bidirectional 10Gbps transfer within 3.3 ft
- Multi-Modal Models: Generative AI for Text, Image, and Audio — Intermediate. Surveys model capabilities across these modalities.
- Multi-Modal RAG: Connecting Images, Text, and Data — Advanced/technical. Combines multiple data types in retrieval systems.
- Building Multimodal Search and RAG — Advanced/technical. Focuses on retrieval across text and non-text modalities.
- Building Multimodal Data Pipelines with Snowflake — Intermediate to advanced. Covers preparing image, audio, and video data as inputs for LLM workflows.
- Google AI Agents for Image and Video Generation — Intermediate to advanced. Uses agents to generate, evaluate, and iterate on visual content.
- Voice for AI Agents and Applications — Intermediate. Covers embedded voice, voice-layer, and callable-tool patterns for applications.
Learning paths that avoid random course-hopping
For a nontechnical beginner
- Start with AI Prompting for Everyone to build a foundation in prompt use.
- Try Build with Andrew to prototype with AI assistance.
- Use Generative AI for Everyone for broader capabilities, limitations, and applications.
- Move to ChatGPT Prompt Engineering for Developers if you want more structured prompt patterns.
- Continue to Building Systems with the ChatGPT API only if you are ready for developer-oriented application work.
For a Python developer building LLM applications
- Use AI Python for Beginners if you need a Python refresher.
- Take ChatGPT Prompt Engineering for Developers, then Building Systems with the ChatGPT API.
- Choose a framework path with LangChain for LLM Application Development or a retrieval path with LangChain: Chat with Your Data.
- Study embeddings and vector search, then take Building and Evaluating Advanced RAG Applications.
- Add Function-Calling and Tool Use with LLMs before progressing to an agent workflow.
For a RAG specialist
- Build a mental model of embeddings with Embedding Models: From Architecture to Implementation.
- Study vector storage with Vector Databases: from Embeddings to Applications.
- Build a document-based example with LangChain: Chat with Your Data.
- Measure and improve retrieval using Retrieval Optimization and an advanced RAG evaluation course.
- Only then add knowledge graphs, multimodal inputs, or agentic retrieval if your use case needs them.
For an agent developer
- Learn function calling and tool use first.
- Move to AI Agents in LangGraph or Building Agentic AI Applications with LlamaIndex.
- Explore multi-agent coordination with Multi AI Agent Systems with crewAI.
- Continue to memory, coding agents, or generative UI only when the application requires those capabilities.
For an ML engineer
- Establish LLM foundations with the Generative AI with Large Language Models course, noting that its free access is a first-module preview.
- Study model adaptation with Finetuning Large Language Models.
- Compare model-size trade-offs with Quantization Fundamentals with Hugging Face.
- Follow an inference path using vLLM or SGLang, then apply what you learn to your own evaluation and deployment constraints.
Prerequisites, costs, and limits to keep in mind
- Programming: Introductory prompting and business-oriented material can be approachable without coding. API, RAG, agent, fine-tuning, and inference work commonly benefits from Python and basic software-development experience.
- Third-party usage: Model APIs can bill by usage; cloud notebooks, GPUs, hosted vector databases, and subscriptions can also have costs. Check current terms with the provider and set usage limits before experimentation.
- Framework lock-in: LangChain, LlamaIndex, CrewAI, vLLM, SGLang, Pinecone, and Chroma can help you build quickly, but their APIs and hosted offerings may change. Learn the underlying model API, retrieval concepts, and evaluation practices alongside a framework.
- Production readiness: A short course can teach a focused method or prototype, not comprehensively cover production security, scaling, monitoring, governance, and cost control.
- Data and security: Treat retrieved documents and user inputs as potentially untrusted. RAG does not by itself prevent hallucinations or prompt injection; evaluate outputs and tool actions against your use case.
- Changing examples: Model names, SDKs, and interface labels can become outdated. Check current provider documentation if a course example no longer runs as shown.
- Credentials: Course completion evidence is not the same as an accredited qualification; verify any certificate requirements on the course page.
DeepLearning.AI’s catalog continues to change and includes partner-created offerings as well as DeepLearning.AI courses. Use it as the canonical place to check current titles, categories, and access terms. For the platform’s course-format explanation, see its courses information page.
Quick Recap
Bestseller No. 5
Best Value
Anker USB C Hub, 5-in-1 USBC to HDMI Splitter with 4K Display
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- Powerful Pass-Through Charging: Supports up to 85W pass-through charging so you can power up your laptop while you use the hub. Note: Pass-through charging requires a charger (not included). Note: To achieve full power for iPad, we recommend using a 45W wall charger.
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- What You Get: Anker 332 USB-C Hub (5-in-1), welcome guide, our worry-free 18-month warranty, and friendly customer service.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
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