Online Machine Learning Summer Programs for High School Students
Updated: Oct 26
Updated October 2026
TL;DR
This guide covers 10 online machine learning programs for high school students. It includes two free university camps on Zoom from the University of Iowa and Iowa State, and two free self-paced courses from Harvard and Google that you can start any time. It also includes free nonprofit programs from Kode With Klossy and Girls Who Code, and paid options like Stanford AI4ALL ($4,120 online in 2026) and the Stanford AIMI Summer Research Internship. Some programs are built for beginners, while others expect you to know Python. If you'd like to build your own machine learning research project with a PhD mentor, you can apply to the Lumiere Research Scholar Program.
If you want to learn machine learning without leaving home, an online summer program is a simple place to start. Many are free, and several need no coding experience. You might spend a week learning the basics, two weeks training an image classifier, or a whole summer on a research project. This guide covers 10 options, along with what each one costs and who can apply. If you’re open to traveling, our list of university AI programs for high school students has more ideas.
What does machine learning look like at the high school level?
Machine learning is the part of AI where computers learn patterns from data, so nobody has to write out every rule by hand. Show a model thousands of labeled cat and dog photos, and it slowly figures out which features matter, like ear shape or whisker placement. That's classification, and it's one of the first things many of these programs teach.
At the high school level, you'll usually start with Python and small datasets. From there, you might move on to concepts like linear regression, neural networks, or reinforcement learning, which is how a game-playing bot improves by trial and error. Most of the work happens in your browser or on a regular laptop.
Live class, self-paced course, or research program: which format suits you?
Live online camps like the University of Iowa AI Youth Camp, the Iowa State High School Summer AI Bootcamp, Stanford AI4ALL, and Kode With Klossy meet on a set schedule with instructors. They work well if you like structure and want to learn alongside other students.
Self-paced courses like CS50's Introduction to Artificial Intelligence with Python, Google's Machine Learning Crash Course, and Girls Who Code Pathways let you set your own pace. They're a good fit if your summer is already busy.
Research and mentorship programs like the Lumiere Research Scholar Program, Veritas AI, and the Stanford AIMI Summer Research Internship have you work on a real project with guidance from mentors.
Which programs match your starting point?
Your starting point | Programs to look at |
Brand new to coding and AI | |
Comfortable with some Python | |
Ready for research or your own project |
Online Machine Learning Summer Programs for High School Students
Location: Remote , you can participate in this program from anywhere in the world!
Cost: Varies depending on program type. Full financial aid available.
Application Deadline: Varies by cohort.
Program Dates: Vary by cohort: summer, fall, winter, or spring. Options range from 12 weeks to 1 year.
Eligibility: You must be currently enrolled in high school and demonstrate a high level of academic achievement.
In the Lumiere Research Scholar Program, you work one-on-one with a PhD fellow or postdoc to write a college-level academic paper. The 12-week Individual Research Program includes 9 sessions with your mentor and 2 with a writing coach. Computer science and data science are both listed research areas, so a machine learning question fits well. If you complete a program, you can earn 3 post-baccalaureate credits through UC San Diego Extended Studies.
Who is it right for? Students who have an ML question in mind and want to turn it into a finished research paper.
2. Veritas AI
Location: Virtual
Cost: Varies depending on program type. Full financial aid available.
Application deadline: Rolling. Spring (January), Summer (May), Fall (September), and Winter (November). You can apply to the program here.
Program dates: Multiple 12-15-week cohorts throughout the year, including spring, summer, fall, and winter.
Eligibility: High school students. AI Fellowship applicants should either have completed the AI Scholars program or exhibit experience with AI concepts or Python.
In AI Scholars, you take live group classes with about five students per mentor, covering Python and core AI ideas before you build a group project. In the AI Fellowship, you work one-on-one with a mentor across 15 sessions to scope, build, and train your own model. You also get 4 sessions with a publication specialist. For a closer look, read our review of Veritas AI.
Who is it right for? Students who want a guided path from ML basics to a model they designed themselves.
Location: Online
Cost: A fee is charged (amount not posted); financial aid offered
Acceptance rate/cohort size: Not published
Program Dates: TBA (previous cohort: two two-week sessions in summer)
Application Deadline: February 1
Eligibility: Students attending a U.S. high school and entering grades 9 to 12 in the fall
The Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI) runs this two-week virtual internship on machine learning in healthcare. In the most recent cohort, topics included ML basics, medical imaging AI, datasets, model evaluation, and responsible AI. You work on a team doing mentored research with real-world health data. The program is designed for students with a strong foundation in computer science, math, or biology.
Who is it right for? Students with some technical background who want to see how ML helps doctors read scans and make decisions.
Location: Online
Cost: Free
Acceptance rate/cohort size: Limited to 30 participants
Program Dates: TBA (previous cohort: June 8 to 19)
Application Deadline: TBA
Eligibility: High school students in grades 9 to 11; basic coding skills required
This two-week camp is run by engineering professors at the University of Iowa College of Engineering. You study machine learning, reinforcement learning, and engineering ethics. You work with open-source datasets from the National Science Foundation, the Office of Naval Research, and NASA. With only 30 spots, you get a small-group feel even though the whole camp is on Zoom.
Who is it right for? Students who already code a little and want to apply ML to real government datasets.
Location: Online
Cost: Free to audit; optional paid verified certificate
Acceptance rate/cohort size: Not published
Program Dates: Self-paced; about 7 weeks long
Application Deadline: None; available now
Eligibility: Anyone who has taken CS50x or has prior programming experience in Python
This free Harvard course is taught by Professor David J. Malan. It covers the ideas behind game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, you learn classification, optimization, and reinforcement learning, then build them into your own Python programs. You can stretch it across the summer or finish it in a few focused weeks.
Who is it right for? Students who know Python and want a rigorous, university-level course on their own schedule.
Location: Online (a residential option on Stanford's campus is also offered)
Cost: $4,120 for the online program (2026); need-based financial aid available
Acceptance rate/cohort size: Not published
Program Dates: TBA (previous cohort: June 15 to 26, online)
Application Deadline: TBA (previous cohort: February 6)
Eligibility: Students in 9th grade at the time of applying who will be older than 14 and younger than 16 during the program
Stanford AI4ALL is hosted by the Stanford Institute for Human-Centered Artificial Intelligence and Stanford Pre-Collegiate Studies. Over two weeks, you take live classes Monday through Friday and work on hands-on AI projects with Stanford-affiliated instructors and mentors. You need to attend all live sessions. If you successfully complete the program, you receive a certificate of completion.
Who is it right for? Ninth graders who want an early, structured introduction to AI and ML from Stanford.
Location: Online (virtual camps; in-person camps are also offered in select cities)
Cost: Free
Acceptance rate/cohort size: Not published
Program Dates: TBA (previous cohort: two-week camps between June and August)
Application Deadline: TBA
Eligibility: Young women, gender-nonconforming, and trans students ages 13 to 18
Kode With Klossy runs free two-week coding bootcamps, and one of its four tracks focuses on artificial intelligence and machine learning. In that track, you design image classifiers and train models with Apple's CreateML framework. You also learn how bias can sneak into AI systems and what you can do about it. After camp, you join the Kode With Klossy community for ongoing free programs and events.
Who is it right for? Beginners who want a short, live camp where they train their first model with a team.
Location: Online (Zoom)
Cost: Free
Acceptance rate/cohort size: Not published
Program Dates: TBA (previous cohort: July 13 to 17, 2026, 9 a.m. to noon)
Application Deadline: TBA
Eligibility: High school students; no prior experience required
This five-day bootcamp is led by faculty from Iowa State's Department of Computer Science. You get an introduction to AI and basic machine learning concepts, then work through hands-on activities and guided exercises on practical uses of AI. Sessions run in the morning, so the rest of your day stays free. You earn a certificate when you finish.
Who is it right for? Complete beginners who want a short, free first look at ML before committing to anything longer.
Location: Online
Cost: Free
Acceptance rate/cohort size: Not published
Program Dates: Self-paced; start any time
Application Deadline: None
Eligibility: Anyone; Google recommends comfort with linear equations, graphs, histograms, and averages, plus some Python experience
Google's Machine Learning Crash Course teaches you ML through short videos, interactive visualizations, and coding exercises. Modules cover linear regression, logistic regression, classification, working with data, and neural networks. The exercises run right in your browser in Google Colab, so you don't need to install anything. Each module stands on its own, so you can skip around once you know the basics.
Who is it right for? Students who like learning on their own and want to understand the math behind how models learn.
Location: Online
Cost: Free
Acceptance rate/cohort size: Not published
Program Dates: TBA (previous cohort: 6 to 7 weeks in summer)
Application Deadline: TBA
Eligibility: Girls and non-binary students in grades 9 to 12, including rising 9th graders, Girls Who Code Summer Programs alums, and graduating seniors
Pathways is a free program you complete at your own pace over 6 to 7 weeks. You can choose tracks in AI and data science, along with options like cybersecurity, game design, and web development. Each course takes about 3 to 6 hours, and you get support through advisor events, student hours, and a Discord community. You earn a certificate for each course you complete.
Who is it right for? Students who want to explore AI and data science at their own pace alongside other summer plans.
Which programs fit your budget?
Cost | Programs | Good to know |
Free | Some have grade, age, or gender rules, so check eligibility first | |
Free to audit | A verified certificate costs extra | |
Paid, with financial aid | Apply for aid when you apply, since aid deadlines can come first |
If you're new to ML, you could take the free Iowa State bootcamp in July and start CS50 AI right after. That way, you finish the summer with both a quick intro and a full course behind you.
Once you've trained a few models, real-world projects are a good next step, and our list of online AI internships for high school students covers remote options. If you'd like a campus experience next summer, our guide to university AI programs for high school students includes programs at Carnegie Mellon and Princeton.
And if you want to round out your skills beyond ML, take a look at our list of online computer science programs for high school students.
5 Ways to Get Ready for an Online Machine Learning Program
1. Get comfortable with Python basics
Most ML work happens in Python, and several programs on this list expect you to know a little. Spend a few weekends on variables, loops, lists, and functions. You'll spend camp time on models and less time fixing typos.
2. Play with a model before you build one
Plenty of free browser tools let you train a simple image classifier with your webcam in a few minutes. Try teaching one to tell your thumbs-up from your thumbs-down. It's a quick way to see how training data shapes what a model learns.
3. Pick a problem you actually care about
ML gets more fun when you're predicting something you care about. Maybe you want to guess which songs will trend or spot patterns in your school's lunch line wait times. Having a question ready also makes application essays easier to write.
4. Line up your recommender early
Some programs need a teacher recommendation. Stanford AI4ALL, for example, requires one from a math or science teacher. Ask in November or early December, and give your teacher a short summary of what you've been learning.
5. Block out your summer calendar
Live camps meet at set hours, and self-paced courses are easy to push off until August. Write down your start dates now, and set weekly goals for any self-paced course so you actually finish it.
The programs on this list are a great way to learn how machine learning works. If you'd like to go further and test an ML idea of your own, the Lumiere Research Scholar Program pairs you one-on-one with a PhD fellow or postdoc from a top university. In the 12-week program, you develop an independent project and finish with a college-level research paper.
Questions high schoolers ask about these machine learning programs
1. Do I need calculus or linear algebra for these machine learning programs?
No, not for any of them. One self-paced course recommends being comfortable with linear equations, graphs, and averages. A couple of the research options look for a solid foundation in math or computer science.
2. Will I actually train a model, or mostly learn how ML works?
In most of these, you train something yourself. You might build an image classifier, write Python programs that learn from data, or run models on real health or government datasets. The shortest bootcamps lean more toward guided activities and core concepts.
3. How competitive are these machine learning programs?
It varies a lot. The self-paced courses are open to anyone, while one university camp capped its 2026 cohort at 30 students, and one mentor program reported offering spots to about 3 in 10 applicants in 2025. For more options to compare, see our list of AI summer camps for high school students.
4. Will I work on my own project or a team project?
It depends on the program. Most of the camps and research internships are team-based, while a couple of the mentor programs let you build a project entirely on your own. If you'd like to lead your own study from start to finish, the Lumiere Research Scholar Program pairs you one-on-one with a PhD mentor to write an independent research paper.