AI for Everyone
The original non-technical introduction to AI from Andrew Ng — what AI can and can't do, how to spot opportunities in your team, and how to talk to engineers.
Hand-curated best online AI courses and certifications with direct enrollment links.
The original non-technical introduction to AI from Andrew Ng — what AI can and can't do, how to spot opportunities in your team, and how to talk to engineers.
Hands-on walk through the full LLM lifecycle: pretraining, fine-tuning, RLHF, and deployment. The deepest free-tier intro to how production LLMs actually work.
The 90-minute short course every developer should take. Concrete prompting patterns — summarizing, inferring, transforming, chaining — with runnable Jupyter examples.
The modernized successor to Andrew Ng's legendary ML course. Three courses covering supervised learning, advanced algorithms, and unsupervised learning + recommender systems.
Five-course deep dive into neural networks, CNNs, sequence models, and transformer fundamentals. The standard prerequisite credential for ML engineering roles.
Project-driven AI bootcamp: build a self-driving car, a Doom-playing bot, and an LLM-powered assistant from scratch. Heavy on intuition, light on math.
Build a real RAG pipeline from the LangChain creator himself: document loaders, embeddings, vector stores, retrieval, and conversational memory.
Production patterns for multi-step LLM apps: input classification, moderation, chain-of-thought, evaluation, and safe deployment. Pairs perfectly with the prompt-eng short course.
Harvard's seven-week deep-dive into the algorithms behind AI: search, knowledge representation, optimization, neural networks, and natural language processing — all in Python.
Top-down, code-first deep learning from Jeremy Howard. Train a state-of-the-art image classifier in lesson 1, then work down to the fundamentals as you need them.
Four-course specialization from Wharton on AI strategy, people analytics, marketing, and finance. Designed for managers who need to make AI bets, not write code.
The patterns-first prompt engineering course. Persona pattern, flipped interaction, question refinement, recipe pattern — taught by the professor who named them.
Apply deep learning to real medical imaging tasks — chest X-rays, MRI segmentation, survival prediction. The first course in the AI for Medicine specialization.
Customer segmentation, attribution, churn prediction, and personalization — every analytics workflow a modern marketer needs, now with LLM-assisted analysis.
Microsoft's official AI-900 certification path. Eight hours of self-paced modules covering ML, computer vision, NLP, and generative AI on Azure — free, with a paid exam.
Google Cloud's free primer on generative AI: what it is, how LLMs and diffusion models work, where the capabilities and limits are, and the responsible-AI considerations that come with deploying it.
Google Cloud's short course on how text-to-image systems actually work: diffusion models, how a prompt becomes pixels, and where image generators like Imagen excel and fall short.
Build a diffusion model from the ground up — sampling, training, and adding context — to understand the engine behind Midjourney, DALL·E, and Stable Diffusion. Hands-on, code-first.
A hands-on intro to Midjourney: turn text prompts into striking images, learn the prompt structure and parameters that control style, and explore different visual looks. Practical, tool-first.
Prepare for the Microsoft Azure AI Engineer Associate certification exam with this course, covering AI and machine learning concepts, including data processing, model deployment, and solution implementation. You'll learn how to design and implement AI solutions using Microsoft Azure services.
Learn the basics of AI, including machine learning, deep learning, and natural language processing, with this introductory course from Microsoft Learn. You'll explore AI concepts, tools, and techniques to get started with building your own AI solutions.
This intermediate-level course covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and neural networks. Students will learn how to apply machine learning algorithms to real-world problems.
This comprehensive course covers AI and machine learning concepts, including deep learning, natural language processing, and computer vision, with hands-on projects and real-world applications.
Duke University's specialization for product managers leading AI and machine-learning initiatives. It covers the machine-learning project lifecycle, how to scope and manage data-science work, the human factors in deploying AI products, and how to collaborate effectively with technical teams to ship responsible, high-impact products.
This course covers the basics of Python programming, including data types, functions, and data structures, with a focus on practical applications in AI and data science. By the end of the course, you'll be able to write your own Python programs and start exploring AI development.
Google's practical, no-code course on writing effective prompts: a repeatable framework for structuring a prompt, iterating on the output, and using AI for everyday work — summarizing, drafting emails, brainstorming, and analysis. Built for beginners across any role.
IBM's beginner introduction to prompt engineering: core techniques (zero-shot, few-shot, chain-of-thought), common prompt patterns, and hands-on practice with tools like the watsonx Prompt Lab. A short, foundational primer.