Intro to Machine Learning
About this free Intro to Machine Learning course
Intro to Machine Learning is a free machine learning course for high school students that unlocks the power of data. You'll discover how computers learn to recognize patterns, make predictions, and even beat humans at games, going from the basic question "how does a machine learn?" to the algorithms real data scientists use.
The course covers the full ML lifecycle, from defining a problem and preparing data to training, testing, and evaluating a model. Along the way you'll meet linear and logistic regression, decision trees, random forests, clustering, reinforcement learning, and neural networks, and learn to read a confusion matrix to tell whether a model is actually any good. No prior AI experience is needed, just curiosity.
Who this course is for
This course is designed for students in grades 8 through 12. You'll need Algebra 1. Knowing some Python helps if you want to try building models yourself, but it isn't required to follow the concepts.
Course outline
- Module 1: What is machine learning? Why ML matters, how machines learn, the ML lifecycle, problem definition, data preprocessing, features, and model evaluation.
- Module 2: Supervised learning. Classification vs. regression, linear and logistic regression, decision trees, and random forests.
- Module 3: Unsupervised learning. How machines find hidden patterns on their own, feature engineering, and clustering.
- Module 4: Reinforcement learning. How agents learn by trial, error, and reward.
- Module 5: AI in the real world. Bias and fairness in AI, everyday ML, ML in science and health, and student projects.
- Deep learning and evaluation. Neural networks, forward propagation, backpropagation, confusion matrices, and what training and testing a model really mean.
What you'll learn
- Walk through each stage of the machine learning lifecycle
- Choose between classification and regression for a given problem
- Explain how decision trees, random forests, and clustering work
- Describe how neural networks learn through backpropagation
- Evaluate a model with a confusion matrix and spot biased results
How long it takes
Plan on about 8 hours, or roughly 90 minutes per module. Students who build their own models in Python should budget extra time.
Frequently asked questions about Intro to Machine Learning
Is Intro to Machine Learning really free?
Yes. Intro to Machine Learning is completely free, with no fees, subscriptions, or account required. STEM for Others is a 501(c)(3) nonprofit, and every course in our curriculum library is free for students, families, and teachers.
How is this different from Introduction to ML?
Introduction to ML is a short, no-code overview of the big ideas. Intro to Machine Learning goes further into specific algorithms, neural networks, and model evaluation.
How do I start the course?
Click Begin Course at the top of this page to open the full slide deck in a new tab. You can also flip through the slides embedded above. Work through one unit at a time and try the practice questions before moving on.
Do I get a certificate?
The self-paced Intro to Machine Learning materials don't include an official certificate. If you're taking the course through a STEM for Others chapter program, ask your chapter leader about recognizing your completion.
Can teachers and clubs use this curriculum?
Yes. Teachers, homeschool families, libraries, and after-school clubs are welcome to use the Intro to Machine Learning materials with their students at no cost. STEM for Others volunteers also teach our curriculum in partner schools and libraries. You can find a chapter near you or start one at your school.
Related free courses
