Machine Learning Course
Learn how machine learning models are actually built and evaluated — supervised and unsupervised learning, the classic algorithms, and how to tell whether a model is any good.
Built for Learners With Some Python
This course assumes basic Python. If you're new to programming, start with the Python for AI course first.
Students With Some Python
Move from writing scripts to building models that learn from data.
Working Professionals
Add core ML skills to an existing analytics, engineering or data role.
Career Switchers
Build a portfolio of ML projects on the way to a role in AI/ML engineering.
What You'll Learn
A practical path from core algorithms to evaluating and shipping a working model.
Supervised Learning
Regression and classification with the classic algorithms — linear/logistic regression, decision trees, and more.
Unsupervised Learning
Clustering and dimensionality reduction for finding structure in data without labels.
Model Evaluation
Train/test splits, cross-validation and the metrics that tell you whether a model actually works.
Capstone Project
Build, evaluate and present a complete ML model on a real dataset, with instructor feedback along the way.
How the Course Runs
Live online classes with an instructor, plus recordings for anything you need to revisit. Small enough batches that you can actually ask questions and get answered.
You'll work on a hands-on ML project throughout the course, not just at the end, and get a certificate once you complete the requirements.
See Full ProcessBefore You Enroll
Do I need to know how to code?
Yes — this course assumes basic Python. If you're starting from zero, the Python for AI course is the right place to begin.
What will I be able to do after this course?
Build, evaluate and compare machine learning models on real datasets using the standard supervised and unsupervised techniques.
How does this relate to the Deep Learning course?
This course covers classic ML — the algorithms and evaluation techniques you should know first. The Deep Learning course builds on this with neural networks.
What does it cost?
See the pricing page for current course fees.
Ready to Learn Machine Learning?
Talk to an advisor about whether this course fits your background and goals — no obligation.