If you’re interested in AI but not ready to commit to a long paid program or internship, free online courses are a smart choice to consider! You can explore the subject on your own time and figure out whether it’s something you want to take further.
The learning here is very hands-on. You don’t just read about models, you run them. You test inputs, see outputs, and slowly understand how systems behave. That trial-and-error approach is what makes things stick.
What do free online AI courses for high schoolers include?
Free online AI courses for high school students typically cover basic coding, working with data, and simple machine learning concepts. You may learn how to prepare data, train a basic model, and evaluate how well it performs. There is usually some form of project or exercise included, where you apply what you’ve learned. This could be something small, like building a simple model or analyzing a dataset.
By the end, you have a clearer sense of how AI works and what skills are involved. Once you build your core skills, you can also check out one of the programs here to further enhance your AI knowledge. We also have some recommendations on interesting research questions in AI you can pursue once you feel ready to build your chops.
With that, here are 15 AI courses for high school students!
15 Free Online AI Courses for High School Students
1. Harvard CS50’s Introduction to Artificial Intelligence with Python
Location: Online via Harvard OpenCourseWare / edX
Cost: Free, with an optional verified edX certificate for $299
Acceptance Rate / Cohort Size: Open enrollment
Dates: Self-paced, available year-round
Application Deadline: None
Eligibility: Open to all
Harvard CS50’s Introduction to Artificial Intelligence with Python allows you to learn how AI systems are built by working through core concepts like search algorithms, machine learning, neural networks, and natural language processing. You’ll follow lectures and complete weekly problem sets where you build small programs such as game-playing systems or recognition tools. The course is structured around coding, so you spend time writing and testing Python code while using libraries like scikit-learn and TensorFlow. Each week builds on the previous one, so you gradually move from basic ideas to more complex systems. By the end, you’ll have worked on multiple projects that show how AI concepts are applied in practice. You’ll build programming skills, problem-solving ability, and a clear understanding of how AI systems work.
2. MIT 6.S191: Introduction to Deep Learning
Location: Online
Cost: Free
Acceptance Rate / Cohort Size: Open enrollment
Dates: Weekly online lectures released each Monday
Application Deadline: None for online access
Eligibility: Open to all, though basic calculus, linear algebra, and familiarity with Python are recommended
MIT 6.S191: Introduction to Deep Learning allows you to explore deep learning through lectures and hands-on labs where you build and train models using TensorFlow. You’ll study neural networks, computer vision, natural language processing, and large language models while applying these ideas in practical tasks. The course includes lab sessions where you work with real data and understand how models are trained and improved. Toward the end, you can take part in a project proposal that receives feedback from experts and industry professionals. The content is updated regularly, so you are working with current methods. You’ll build technical understanding, coding skills, and experience working with modern AI tools.
3. Elements of AI: Introduction to AI (University of Helsinki)
Location: Online
Cost: Free
Acceptance Rate / Cohort Size: Open enrollment, with 2+ million learners globally
Dates: Self-paced, available year-round
Application Deadline: None
Eligibility: Open to all
Elements of AI: Introduction to AI (University of Helsinki) allows you to learn the basics of artificial intelligence through short modules that explain how AI systems work without requiring coding experience. You’ll go through topics like machine learning, neural networks, and real-world AI applications using simple explanations and interactive exercises. The course is self-paced, so you can move through lessons based on your own schedule. Each section includes tasks that help you apply what you’ve learned rather than just reading through content. You’ll also explore ethical questions and how AI affects society. You’ll build foundational knowledge and a clear understanding of AI concepts.
4. Elements of AI: Building AI (University of Helsinki)
Location: Online
Cost: Free
Acceptance Rate / Cohort Size: Open enrollment
Dates: Self-paced, available year-round
Application Deadline: None, though you need to complete course assignments and enroll by August 31 to be eligible for credits.
Eligibility: Open to all
Building AI goes deeper than the introductory course, taking you into the actual algorithms that make AI systems work. You will cover regression, classification, neural networks, and optimization through exercises that include beginner, intermediate, and advanced difficulty levels. The intermediate and advanced exercises involve writing Python code to implement core algorithms from scratch, which builds a more concrete understanding of how models actually learn from data. To pass the course, you need to complete at least 19 of the 21 exercises, and the certificate distinguishes between standard, intermediate honors, and advanced honors based on the level of exercises you complete. The course was built by the same team behind Introduction to AI and continues the same mix of conceptual clarity and hands-on practice, this time with a stronger technical focus.
5. IBM SkillsBuild: AI Foundations (ISTE and IBM)
Location: Online
Cost: Free
Acceptance Rate / Cohort Size: Open enrollment
Dates: Self-paced, available year-round
Application Deadline: None
Eligibility: Students ages 13 to 18
Developed as a partnership between ISTE and IBM, the AI Foundations course on IBM SkillsBuild is built specifically for high school students and takes roughly 14 to 15 hours to complete. You will move through modules that cover the basics of how AI systems learn from data, how AI is applied across industries, career pathways in the field, and ethical considerations around AI development and use. The course also integrates IBM’s design thinking methodology, asking you to apply what you learn to real-world problem framing. You will work through exercises and assessments throughout, and after completing the course, you will earn an IBM-issued digital badge that you can share on LinkedIn or include in college applications.
6. Google AI Essentials (Coursera)
Location: Online
Cost: Free to audit; Google certificate available through financial aid or the Google Career Certificate Scholarship
Acceptance Rate / Cohort Size: Open enrollment
Dates: Self-paced, available year-round
Application Deadline: None
Eligibility: Open to all; no prior experience required
Google AI Essentials (Coursera) allows you to learn how generative AI tools work and how to use them in everyday tasks through a series of short, practical courses. You’ll explore topics like prompt writing, bias in AI systems, and how AI is applied in real workflows such as summarising documents or generating ideas. The program includes hands-on activities where you use AI tools directly rather than just learning theory. It is designed to be completed quickly, with flexible pacing. You’ll build practical skills, tool familiarity, and an understanding of responsible AI use.
7. Stanford and DeepLearning.AI: Machine Learning Specialization (Coursera)
Location: Online
Cost: Free to audit
Acceptance Rate / Cohort Size: Open enrollment
Dates: Self-paced, available year-round
Application Deadline: None
Eligibility: Open to all; High school math and basic Python knowledge preferred
Stanford and DeepLearning.AI: Machine Learning Specialization (Coursera) allows you to learn how machine learning models are built and applied through a structured series of courses that cover both concepts and coding. You’ll study supervised learning, neural networks, decision trees, and recommendation systems while also working with Python libraries like NumPy and scikit-learn. The program includes video lectures along with optional labs and assignments where you apply algorithms to real datasets. Each course builds on the previous one, so you gradually move from basic models to more complex systems. You’ll build technical understanding, coding skills, and a clear sense of how machine learning works in practice.
8. Fast.ai: Practical Deep Learning for Coders
Location: Online
Cost: Free
Acceptance Rate / Cohort Size: Open enrollment
Dates: Self-paced, available year-round
Application Deadline: None
Eligibility: Open to all, though at least one year of coding experience (preferably in Python) and high school-level math are required
Fast.ai: Practical Deep Learning for Coders allows you to learn deep learning by starting with real applications and then working back toward the underlying concepts, rather than beginning with theory. You’ll build models for tasks like image recognition, natural language processing, and recommendation systems using PyTorch and the fastai library. The course runs through practical examples in coding notebooks, so you’re actively working with models instead of just watching lectures. As you progress, you begin to understand how these models function and how to improve them. You’ll build coding skills, practical experience, and a deeper understanding of deep learning systems.
9. MIT OpenCourseWare: Introduction to Machine Learning (6.036)
Location: Online
Cost: Free
Acceptance Rate / Cohort Size: Open enrollment
Dates: Self-paced, available year-round
Application Deadline: None
Eligibility: Open to all, though calculus, linear algebra, and basic Python knowledge are helpful to have
MIT’s 6.036 Introduction to Machine Learning is a full undergraduate course published through MIT’s Open Learning Library, covering machine learning from the perspective of modeling and prediction. You will study supervised learning, reinforcement learning, overfitting, generalization, and feature engineering through lectures, notes, exercises, labs, and homework problems. The course applies these ideas to image recognition tasks and temporal sequences, grounding the concepts in concrete applications. You can enroll to track your progress and access all assignments, or browse the materials anonymously. Though MIT’s Open Learning Library does not award certificates for 6.036, the depth of the curriculum and the MIT authorship make it one of the most academically rigorous free ML resources available to high school students.
10. Girls Who Code: Pathways (Data Science + AI and AI + Society tracks)
Location: Online
Cost: Free
Acceptance Rate / Cohort Size: Selective
Dates: June 29 – August 14
Application Deadline: April 10
Eligibility: Girls and non-binary students in grades 9 through 12, including rising 9th graders and graduating seniors
Girls Who Code Pathways is a free, flexible seven-week virtual program offering coursework across five tracks, two of which are AI-specific: Data Science + AI and AI + Society. In the Data Science + AI track, you will learn how data is collected, processed, and used to train intelligent systems, building hands-on projects in a self-paced online environment. The AI + Society track takes a more analytical angle, asking you to examine the ethical, social, and cultural impact of AI through scenario-based exercises and critical thinking tasks. Each course within a track takes three to six hours to complete, and you will earn a certificate for every course you finish. Optional corporate partner events, career panels, and advisor-led workshops run throughout the program, connecting you with professionals at tech companies. You will also join Girls Who Code’s alumni network upon completing at least one course.
11. Google AI Essentials for Education (Grow with Google)
Location: Online (grow.google/ai-for-educators)
Cost: Free
Acceptance Rate / Cohort Size: Open enrollment
Dates: Self-paced, available year-round
Application Deadline: None
Eligibility: Open to all. Though it’s targeted at educators, it is accessible to advanced high school students.
Google’s two-hour Generative AI for Educators course, built by Google’s AI team and distributed through Grow with Google, walks through what generative AI is, how tools like Gemini and NotebookLM work in practice, and how to use them responsibly. While the course is designed with teachers in mind, the content is entirely accessible to high school students who want a practical, tools-first introduction to generative AI without any technical prerequisites. You will complete hands-on exercises using Gemini for tasks like drafting and summarizing, understand the opportunities and limits of generative AI outputs, and finish with a certificate of completion. For students already familiar with AI basics, this course offers a clear lens into how large language model-based tools are actually applied in real-world settings.
12. DeepLearning.AI: Deep Learning Specialization
Location: Online
Cost: Free to audit
Acceptance Rate / Cohort Size: Open enrollment
Dates: Self-paced, available year-round
Application Deadline: None
Eligibility: Open to all. Some knowledge of Python and basic linear algebra/calculus is recommended
The Deep Learning Specialization on Coursera is a five-course program that covers neural networks, convolutional networks, sequence models, and the practical side of structuring machine learning projects. You will build models for image recognition, speech recognition, music generation, and natural language processing across the five courses, progressing from feedforward networks through recurrent networks and transformer architectures. The specialization was recently updated and continues to reflect current practice in applied deep learning. All lecture videos can be accessed for free by auditing the courses, and graded programming assignments in Python using TensorFlow are available through financial aid or paid enrollment.
13. MIT OpenCourseWare: Artificial Intelligence (6.034)
Location: Online (ocw.mit.edu)
Cost: Free
Acceptance Rate / Cohort Size: Open enrollment
Dates: Self-paced, available year-round
Application Deadline: None
Eligibility: Open to all
MIT OpenCourseWare: Artificial Intelligence (6.034) allows you to study the core methods of AI through a complete set of lectures, notes, and problem sets taken from MIT’s undergraduate curriculum. You’ll learn about topics like search algorithms, knowledge representation, constraint solving, and probabilistic reasoning, while also exploring machine learning and natural language processing. The course includes structured exercises that help you apply each concept, so you are not just learning theory. You can follow the content at your own pace while working through materials used in an actual MIT course. You’ll build strong conceptual understanding and problem-solving skills in AI.
14. CS50 AP – Harvard
Location: Online
Cost: Free
Acceptance Rate / Cohort Size: Open enrollment
Dates: Available throughout the academic year
Application Deadline: None
Eligibility: Current high school students
CS50 AP (Harvard University) allows you to learn computer science through a structured course that also introduces key AI concepts within a broader programming curriculum. You’ll work with languages like C, Python, SQL, and JavaScript while also exploring topics such as machine learning, neural networks, and generative AI. The course includes problem sets and projects that require you to apply what you learn through coding. You also interact with an AI-based assistant that helps guide your work. The structure follows an academic format that aligns with AP Computer Science Principles. You’ll build programming skills, logical thinking, and a basic understanding of AI systems.
15. Microsoft Learn: AI Fundamentals Learning Path
Location: Online
Cost: Free
Acceptance Rate / Cohort Size: Open enrollment
Dates: Self-paced, available year-round
Application Deadline: None
Eligibility: Open to all
Microsoft Learn: AI Fundamentals Learning Path allows you to learn AI concepts through a series of short modules that explain topics like machine learning, computer vision, natural language processing, and generative AI in a structured way. You’ll work through interactive lessons and small exercises that show how these systems function, often using Microsoft Azure tools as examples. The course is designed for beginners, so you can move through it without prior experience. Each module builds on the previous one, helping you develop a complete understanding of the field. At the end, you can prepare for the AI-900 certification exam if you choose. You’ll build foundational knowledge, technical awareness, and familiarity with AI systems.
One more option – Horizon Academic Research Program
If you’re looking for a competitive mentored research program in subjects like data science, machine learning, political theory, biology, and chemistry, consider applying to Horizon’s Research Seminars and Labs! This is a selective virtual research program that lets you engage in advanced research and develop a research paper on a subject of your choosing. Horizon has worked with 1000+ high school students so far and offers 600+ research specializations for you to choose from. You can find the application link here!
Image source: University of Helsinki