Free courses from universities and nonprofits are enough to build a working foundation in artificial intelligence. A workable order is a plain-language overview with no programming, then Python, then the core math, then machine learning with code, then deep learning and responsible use. The time figures on this page are illustrative planning estimates, not course requirements.
Key takeaways
- The first stage needs no programming: Elements of AI, from the University of Helsinki and MinnaLearn, is free and self-paced.
- Python and a little linear algebra, probability, and statistics come before serious machine learning work.
- Harvard's CS50 AI and the Practical Deep Learning course are free and assume about a year of Python.
- Every resource named here was opened and checked in October 2026, but course content and prerequisites change, so the course pages remain the final word.
The learning path at a glance
The table lays out seven stages. Time estimates are illustrative and assume a few hours of study per week; faster or slower progress is normal, and stages three and four can run side by side.
| Stage | Topic | Prerequisite | Resource | Time (illustrative) |
|---|---|---|---|---|
| 1 | What AI is and what it can do | None | Elements of AI (University of Helsinki, MinnaLearn) | 4 to 6 weeks |
| 2 | Python programming basics | None | The Python Tutorial (Python Software Foundation) | 3 to 4 weeks |
| 3 | Linear algebra, probability, statistics | High school algebra | MIT OpenCourseWare 18.06 and 18.05 | 8 to 12 weeks |
| 4 | Machine learning fundamentals | Python basics, algebra, simple statistics | Google Machine Learning Crash Course; scikit-learn user guide | 4 to 6 weeks |
| 5 | AI algorithms in code | CS50x or about a year of Python | Harvard CS50's Introduction to AI with Python | 7 to 10 weeks |
| 6 | Deep learning | About a year of coding, high school math | Practical Deep Learning for Coders | 6 to 10 weeks |
| 7 | Risk and responsible use | None | NIST AI Risk Management Framework | 1 to 2 weeks |
Stage one: understanding AI without code
Elements of AI was created by MinnaLearn and the University of Helsinki and is described as a free online course for anyone curious about what AI is. Its introductory course asks for no complicated math or programming, and it can be finished at the learner's own pace. A European Commission training catalog entry lists six chapters, each split into three sections. A follow-on course, Building AI, recommends some basic Python.
This stage settles vocabulary: the difference between rules-based systems and learning systems, what training data does, and where AI errors come from. Starting here avoids a common stall, where a learner opens a coding tutorial without knowing what problem the code is meant to solve.
Stages two and three: Python and the math underneath
The official Python tutorial, maintained by the Python Software Foundation, is free and covers control flow, data structures, modules, classes, and the standard library. It is a reasonable benchmark for the Python level that the later courses expect.
For math, two MIT OpenCourseWare undergraduate courses are free under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 license: 18.06 Linear Algebra, taught by Gilbert Strang, and 18.05 Introduction to Probability and Statistics. Neither page lists prerequisites. Google's crash course names the topics it expects: tensors and matrix multiplication, mean, median, and standard deviation, algebra with logarithms and the sigmoid function, and, optionally, derivatives and the chain rule. Learners who want less depth can cover just those topics before moving on.
Stages four to six: from machine learning to deep learning
Google's Machine Learning Crash Course has 12 modules in four sections: ML models, data, advanced ML models, and real-world ML. It covers regression, classification, neural networks, embeddings, large language models, production systems, and fairness. Exercises run in the browser through Colaboratory, and the pages open without an account; the course pages state no price, so the terms are worth confirming. The scikit-learn user guide, published under a BSD license, is a practical companion for trying algorithms on real datasets.
CS50's Introduction to Artificial Intelligence with Python has seven weeks of material: search, knowledge, uncertainty, optimization, learning, neural networks, and language. It lists CS50x or at least one year of Python as the prerequisite and can be taken free through OpenCourseWare. Verified and professional certificates are offered through an online course platform, and the course page states no price.
The course Practical Deep Learning for Coders (2022, Part 1) has nine lessons of roughly 90 minutes each, taught by Jeremy Howard. It is free, needs some coding experience and high school math, and runs on free notebook services rather than a personal computer.
Stage seven: risk and responsible use
Technical skill is half of AI literacy. The NIST AI Risk Management Framework, released as version 1.0 on January 26, 2023 by the US National Institute of Standards and Technology, is voluntary guidance on identifying and managing AI risks. Skimming it gives a vocabulary for the questions that follow any deployed system: how reliable it is, whom it may treat unfairly, and who is accountable when it fails.
Learners who plan to build or buy AI tools for a school, clinic, or nonprofit can pair this reading with the machine learning stages.
FAQ
Is a degree needed to start learning AI?
No. Every resource in the table is open to anyone. A degree matters for some research and employer requirements, but the courses here cover the same core ideas as many introductory university classes.
How much math is required?
None for stage one. For machine learning proper, linear algebra, basic probability, and statistics are the working minimum, and calculus is optional until the deep learning material.
Which course should come first?
Elements of AI suits anyone without a technical background. A learner who already writes Python can skip to the Google crash course or CS50 AI.
Are certificates included?
Not always, and policies change. CS50 AI mentions verified and professional certificates through an online course platform, with no price shown on its page. Learners who need a credential should check each course page before enrolling.
Related reading
- AI and machine learning compared
- how an expert system is built
- AI use in classrooms
- risks of leaning too much on AI
More in AI Basics.
Last verified: October 2026
Sources
- Elements of AI, University of Helsinki and MinnaLearn, 2026
- Elements of AI, European Commission Digital Skills and Jobs Platform training catalog, 2026
- CS50's Introduction to Artificial Intelligence with Python, Harvard University, 2026
- Machine Learning Crash Course and prerequisites page, Google for Developers, 2026
- Practical Deep Learning for Coders, 2022 edition
- The Python Tutorial, Python Software Foundation, 2026
- 18.06 Linear Algebra and 18.05 Introduction to Probability and Statistics, MIT OpenCourseWare
- scikit-learn User Guide, scikit-learn developers, 2026
- AI Risk Management Framework, National Institute of Standards and Technology, 2023