Deep Learning Course
Understand how neural networks actually work — from the basics through CNNs, RNNs and transformers — and apply them to real projects, conceptually and in code.
Built for Learners Who Know the ML Basics
This course builds on core machine learning concepts. If you haven't covered those yet, start with the Machine Learning course first.
ML Practitioners Going Deeper
Extend your knowledge of classic ML into neural networks and modern architectures.
Working Professionals
Bring deep learning skills into a data science or engineering role that's outgrowing classic ML.
Career Switchers
Build a deep learning project for your portfolio on the way to an AI/ML engineering role.
What You'll Learn
A practical path from neural network fundamentals to modern architectures and a real project.
Neural Network Foundations
How networks learn — layers, activations, backpropagation and training in practice.
CNNs for Vision
Convolutional networks for image-based tasks, at a conceptual and applied level.
RNNs & Transformers
Sequence models and the transformer architecture behind modern language and vision systems.
Capstone Project
Build and present a working deep learning project end-to-end, 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 deep learning 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 machine learning first?
Yes — this course assumes you're comfortable with core ML concepts. The Machine Learning course is the recommended starting point.
What will I be able to do after this course?
Understand and apply CNNs, RNNs and transformer-based models to real vision and sequence tasks.
Is this heavy on math?
The course covers the concepts you need with intuition-first explanations, then applies them in code — it's not a pure theory course.
What does it cost?
See the pricing page for current course fees.
Ready to Learn Deep Learning?
Talk to an advisor about whether this course fits your background and goals — no obligation.