Grokking Modern AI Fundamentals

What the course covers

The course is organised into 14 chapters, 50 lessons, 12 interactive playgrounds and 9 assessments:

1. Introduction
2. Core concepts in AI: the vocabulary you need to follow any AI discussion.
3. Foundations for AI
4. Building and training a simple machine learning model: seeing the full loop of data, training and prediction.
5. Understanding neural networks and deep learning
6. Search algorithms: the classic AI techniques that still underpin planning and problem solving.
7. The modern AI revolution: how today's large models came about and what changed.
8. Agentic AI, building autonomous agents: how systems that plan and take actions are put together.
9. Reinforcement learning: learning from rewards, and why it matters for modern models.
10. Modern AI tools and prompting techniques
11. Ethical, social and regulatory considerations
12. Breaking down the AI hype cycle: separating real capability from marketing.
13. Brainstorming activities: applying what you learned to your own ideas.
14. AI's future and emerging trends

Format, length and cost

It is text-based with runnable playgrounds and assessments in the browser, so there is nothing to install. The stated study time is about five hours. As of September 2026 the course page lists a one-time price of $72 with lifetime access, a completion certificate, a 4.1 out of 5 rating from 736 reviews, and 2,209 learners.

Who it suits

• Software engineers who are starting to work on AI features and want the concepts in one place before going deeper.
• CS students who want a structured overview of the field before choosing AI or machine learning electives.
• Product-minded engineers and team leads who need to talk confidently about what AI can do, what agents are and where the hype runs ahead of reality.

No prior AI or machine learning knowledge is needed. Basic programming experience helps with the playgrounds.

How to get the most out of it

• Take it in two or three sittings. Chapters 1 to 6 cover foundations; chapters 7 to 10 cover modern AI; chapters 11 to 14 cover ethics, hype and the future.
• Edit every playground. Change inputs and parameters and predict what will happen before you run the code.
• Use the brainstorming chapter seriously. Write down two AI features you could build in a project or at work, and note which techniques from the course each would use.
• Keep a glossary. Writing one-line definitions of each term (model, training, agent, reward, prompt) makes later, more technical material much easier to follow.

What you can do afterwards

You will be able to explain how machine learning models learn, what neural networks and reinforcement learning are, how AI agents act, and how to judge claims about new AI products. From here, natural next steps in the same library are Grokking Prompt Engineering for Software Engineers, to use AI tools more effectively day to day, and Grokking the AI System Design Interview, to design AI-backed systems.

Common questions

Do I need maths or machine learning experience? No. It is a beginner course.

How long does it take? About five hours of study, which most people spread over a week.

Is there a certificate? Yes, on completion.

Best for: engineers and students who want a fast, structured foundation in modern AI concepts before building with them.