15 Online Machine Learning Courses for High School Students
If you're a high school student interested in the growing field of machine learning, then an online machine learning course might benefit you. Online courses allow you to develop practical skills, gain industry experience, and make valuable connections with peers and professionals. Online machine learning courses also offer a flexible learning experience and let you gain knowledge from anywhere in the world.
How are machine learning courses different from other high school programs?
Many online learning platforms, such as edX and Coursera, offer machine learning courses for high school students and beginners. These courses provide you with the opportunity to explore a range of concepts and skills, including training large language models, supervised and unsupervised learning, dimensionality reduction, regression, data preprocessing, and more. These courses allow you to learn at your own pace without the pressure of following a strict schedule.
To make your search easier, we've narrowed down our list to the 15 online machine learning courses for high school students.
If you’re looking for online STEM programs, check out our blog here.
1. HarvardX: Fundamentals of TinyML
Location: Virtual
Cost/Stipend: Free; Certificate Fee (Optional): $299
Dates: Self-paced
Application Deadline: Available year-round
Eligibility: Open to all
Harvard x: Fundamentals of TinyML course introduces high school students to the fundamentals of Tiny Machine Learning. The course will allow you to develop an in-depth understanding of deep learning and embedded devices and systems, including smartphones. You will learn about data science techniques and algorithms for training basic machine learning models. You will have the opportunity to learn from Vijay Janapa Reddi, Associate Professor at the John A. Paulson School of Engineering and Applied Sciences (SEAS) at Harvard University, and gain knowledge of responsible AI design. This introductory course in machine learning does not require any prior experience.
2. Veritas AI
Location: Virtual
Cost: Varies depending on program type. Full financial aid available
Dates: Multiple 12-15-week cohorts throughout the year, including spring, summer, fall, and winter
Application deadline: Rolling. Spring (January), Summer (May), Fall (September), and Winter (November). You can apply to the program here
Eligibility: High school students. AI Fellowship applicants should either have completed the AI Scholars program or exhibit experience with AI concepts or Python
Veritas AI, founded and run by Harvard graduate students, offers programs for high school students who are passionate about artificial intelligence. If you are looking to get started with AI, ML, and data science, you would benefit from the AI Scholars program. Through this 10-session boot camp, you are introduced to the fundamentals of AI & data science and get a chance to work on real-world projects. Another option for more advanced students is the AI Fellowship with Publication & Showcase. Through this program, you get a chance to work 1:1 with mentors from top universities on a unique, individual project. A bonus of this program is that you have access to the in-house publication team to help you secure publications in high school research journals. You can also check out some examples of past projects here and read about a student’s experience in the program here.
3. Google’s Machine Learning Crash Course
Location: Virtual
Cost: Free
Dates: Self-paced
Application Deadline: Available year-round
Eligibility: Open to anyone; Recommended prerequisites: A basic understanding of linear equations, graphs of functions, variables, histograms, and statistical means + experience in Python.
Google’s Machine Learning Crash Course offers high school students an interactive learning experience. The course will introduce you to the fundamentals of building regression and classification models, teach you the best techniques for working with machine learning data, and provide an overview of advanced ML model architectures. You will have the opportunity to learn a range of concepts in machine learning, including Large Language Models, real-world machine learning, productionization best practices, automation, and responsible engineering. You will learn to work with categorical and numerical data, the significance of embedding for machine learning on large feature vectors, and the best ways to use automated machine learning. The course is self-paced and is a great learning opportunity.
4. Lumiere Research Scholar Program - Computer Science Track
Location: Remote , you can participate in this program from anywhere in the world!
Cost: Varies depending on program type. Full financial aid available.
Dates: Varies by cohort: summer, fall, winter, or spring. Options range from 12 weeks to 1 year.
Application Deadline: Varies by cohort. You can apply here.
Eligibility: You must be currently enrolled in high school and demonstrate a high level of academic achievement.
The Lumiere Research Scholar Program is a rigorous research program tailored for high school students. The program offers extensive 1-on-1 research opportunities for high school students across a broad range of subject areas. The program pairs high school students with Ph.D. mentors to work 1-on-1 on an independent research project. At the end of the 12-week program, you’ll have developed an independent research paper! You can choose research topics from subjects such as computer science, psychology, physics, economics, data science, engineering, chemistry, international relations, and more.
5. HarvardX: CS50's Introduction to Artificial Intelligence with Python
Location: Virtual
Cost: Free; Certificate Fee (Optional): $299
Dates: Self-paced seven-week course
Application Deadline: Available year-round
Eligibility: Open to anyone
HarvardX: CS50’s Introduction to Artificial Intelligence with Python is a seven-week, self-paced introductory course suitable for high school students. The course will provide you with knowledge of a range of concepts in modern artificial intelligence and technologies, including machine translation, game-playing engines, and handwriting recognition. You will have the opportunity to work on hands-on projects and develop an in-depth understanding of graph search algorithms, large language models, machine learning, optimization, classification, and more. You will understand reinforcement learning, neural networks, adversarial research, probability theory, logical inference, Markov models, and Bayesian networks. The course is industry-recognized and will allow you to earn a verified certificate from Harvard University.
6. Machine Learning with Python: A Practical Introduction
Location: Virtual
Cost: Free; Certificate Fee (Optional): $99
Dates: Self-paced five-week course
Application Deadline: Available year-round
Eligibility: Open to anyone
Machine Learning with Python: A Practical Introduction is an IBM-verified five-week self-paced course. The course will develop an understanding of supervised and unsupervised learning, explore the relationship between statistical modeling and machine learning, and examine a range of popular algorithms, including dimensionality reduction, classification, clustering, and regression. You will learn about practical applications of machine learning and how it affects society. The course, led by Saeed Aghabozorgi, PhD, Sr. Data Scientist at IBM, is a great learning opportunity for high school students.
7. PyTorch Basics for Machine Learning
Location: Virtual
Cost: Free; Paid Certificate (Optional)
Dates: Self-paced five-week course
Application Deadline: Available year-round
Eligibility: Open to all
PyTorch Basics for Machine Learning is a five-week introductory course. The course will allow you to understand the fundamentals of PyTorch and prepare you to learn more advanced concepts. You will learn about tensor types, PyTorch's automatic differentiation, and integration with Pandas and Numpy. You will have the opportunity to build a machine learning pipeline in PyTorch, load large datasets, train models in PyTorch, and train machine learning applications with PyTorch. The program is taught by Joseph Santarcangelo, PhD, a Data Scientist at IBM, and is a great learning opportunity.
8. Fundamentals of Machine Learning for Healthcare
Location: Virtual
Cost: Free; Paid Certificate (Optional)
Dates: Self-paced
Application Deadline: Available year-round
Eligibility: Open to anyone
Fundamentals of Machine Learning for Healthcare is a beginner-level course offered by Stanford Online. The course will help you understand the relationships among machine learning, biostatistics, and traditional computer programming. You will gain knowledge about advanced neural network structures for a range of tasks, including text classification, segmentation, and object detection. You will understand the impact of dynamic medical practice and discontinuous timelines on the development and deployment of clinical machine learning applications. The course will allow you to develop a range of skills, including healthcare ethics, data preprocessing, model evaluation, applied machine learning, reinforcement learning, and many more.
9. HarvardX: Data Science: Building Machine Learning Models
Location: Virtual
Cost: Free; Paid Certificate (Optional)
Dates: Self-paced
Application Deadline: Available year-round
Eligibility: Open to anyone
HarvardX: Data Science: Building Machine Learning Models is an eight-week self-paced course suitable for high school students. The course will allow you to learn the fundamentals of machine learning, understand regularisation and its importance, perform cross-validation to avoid overfitting, and learn about many popular machine learning algorithms. The course, taught by Rafael Irizarry, Professor of Biostatistics at Harvard University, allows students to develop a range of skills, including data science, forecasting, principal component analysis, speech recognition, and more.
10. Mathematics for Machine Learning
Location: Virtual
Cost: Free; Paid Certificate (Optional)
Dates: Self-paced
Application Deadline: Available year-round
Eligibility: Open to all
Mathematics for Machine Learning is a self-paced beginner-level course offered by Imperial College of London. You will have the opportunity to develop a range of skills, including calculus, statistics, applied mathematics, feature engineering, dimensionality reduction, data preprocessing, regression analysis, mathematical modeling, and more. The course typically spans four weeks but allows you to learn at your own pace. You will also learn a range of tools, including Python Programming, NumPy, and Jupyter. The course offers a great opportunity to learn valuable skills from University and industry experts and earn a recognized certificate from Imperial College London.
11. University of Washington’s Machine Learning Specialization
Location: Virtual
Cost: Free; Paid Certificate (Optional)
Dates: Self-paced
Application Deadline: Available year-round
Eligibility: Open to all; Some related experience required
University of Washington’s Machine Learning Specialization is an intermediate-level self-paced four-course series. The course will introduce you to the field of machine learning, present practical case studies, and teach you to analyze complex datasets. You will have the opportunity to develop a range of skills, including image analysis, text mining, unsupervised learning, predictive modeling, feature engineering, decision tree learning, and many more. You will also learn classification algorithms through this course, along with the opportunity to earn a recognized certificate.
12. Foundations of Machine Learning
Location: Virtual
Cost: Free; Paid Certificate (Optional)
Dates: Self-paced
Application Deadline: Available year-round
Eligibility: Open to all; Recommended prerequisites: Basic familiarity with Python syntax, Data structures, and Linear Algebra concepts like vectors, matrices, dot products, and Eigenvalues
Foundations of Machine Learning is an intermediate-level three-week course. The course offers four modules, including supervised learning, unsupervised learning, data preprocessing and feature engineering, and time series forecasting. The course is instructed by industry professionals and is a part of multiple programs. You will have the opportunity to learn a range of skills, including regression analysis, anomaly detection, predictive modeling, dimensionality reduction, data manipulation, model evaluation, and more. The course is a great opportunity for high school students to learn machine learning concepts on a flexible schedule.
13. Introduction to AI and Machine Learning on Google Cloud
Location: Virtual
Cost: Free
Dates: Self-paced
Application Deadline: Available year-round
Eligibility: Open to all
Introduction to AI and Machine Learning on Google Cloud is a beginner-level course suitable for high school students. You will have the opportunity to gain knowledge about a range of concepts, including the application of generative AI, recognizing the data-to-AI technologies and tools offered by Google Cloud, develop an AI project on Google Cloud from different options, and build ML models end-to-end by using Vertex AI. The course is a part of multiple programs and includes six modules. You will learn a range of skills through the course, including prompt engineering, tensor flows, model deployment, and more.
14. Machine Learning for All
Location: Virtual
Cost: Free
Dates: Self-paced
Application Deadline: Available year-round
Eligibility: Open to all; No prior experience required
Machine Learning for AI is a beginner-level online course offered by the University of London. The course will help you understand the basics of modern machine learning technologies, examine the benefits and harms of machine learning to society, and predict how data affects machine learning results. You will have the opportunity to develop a range of skills, including data collection, data analysis, data preprocessing, image analysis, classification algorithms, artificial intelligence, and model evaluation. The course includes four modules and is a great learning opportunity for high school students.
15. Introduction to Machine Learning with Python
Location: Virtual
Cost/Stipend: Free
Dates: Self-Paced
Application Deadline: Available year-round
Eligibility: Open to all; Recommended experience: Background in basic algebra, statistics, and how a computer works.
Arizona State University’s Introduction to Machine Learning with Python is a beginner-level, four-module course suitable for high school students. The course will allow you to build machine learning models by applying advanced Python coding skills. You will have the opportunity to develop a range of skills in machine learning, including Python programming, predictive modeling, supervised learning, unsupervised learning, image analysis, computer vision, applied machine learning, data processing, and more. The course offers a flexible schedule and is a part of Python: A Guided Journey from Introduction to Application Specialization.