Fundamentals of Deep Learning
Master how neural networks and deep learning power today’s AI systems, with hands-on projects and real-world applications from structured data to computer vision.
Course Information
Certificate Track
About this Course
Dive into the world of neural networks and deep learning to understand how modern AI systems learn from data. This course helps you build a clear understanding of how networks process both structured and unstructured data, giving you the intuition to see how models make predictions and avoid common pitfalls like overfitting and underfitting.
Fundamentals of Deep Learning is one of the five courses in Universal AI, a self-paced program designed to help you go from beginner to AI authority—no coding or technical skills required.
What you'll learn
- Understand the basics of neural networks, including perceptrons, multilayer networks, and embeddings
- Learn how models handle structured and unstructured data
- Build and train neural networks using modern deep learning frameworks
- Apply optimization and regularization techniques to improve performance and avoid overfitting
- Design and evaluate models for real-world tasks like classification and regression
- Explore convolutional neural networks and transfer learning for computer vision applications
Modules
This course has 3 modules
Foundations of Neural Networks
Hands-on Deep Learning
Deep Learning and Computer Vision
How you'll learn
Real-World LearningLearn from MIT faculty and experts who ground their teaching in real-world cases rather than mathematical models, making the material approachable for all.
Practical ApplicationApply your new knowledge with hands-on, practical exercises drawn from healthcare, sports, finance, sustainability, and more.
AI-Enabled SupportDeepen your understanding of the course material and get help on assignments from AskTIM, the AI assistant built by MIT researchers.
Stackable CredentialsEarn an MIT Open Learning certificate at each milestone—module, course, and program—demonstrating your AI expertise. Available in paid courses only.
Meet your instructors
Léonard Boussioux
Assistant Professor, Foster School of Business, University of Washington; Adjunct Assistant Professor in Computer Science, Allen School of Computer Science and Engineering; Affiliate Faculty, Laboratory for Innovation Science at Harvard University
Léonard Boussioux's research and entrepreneurial work have received wide recognition, including giving two TEDx talks, winning the MIT 3-minute thesis competition, and awards from INFORMS, MIT, IEEE, UNESCO, and Google. He previously worked at Google X, Mila, UC Berkeley, and the French National Centre for Scientific Research. Léonard is also a passionate teacher and has received eight teaching awards at the University of Washington and MIT, including the Goodwin Medal.
FAQs
There are no prerequisites for the courses in this program, and no coding background is required.
Yes, you will receive a verified certificate of completion for this course. Additionally, if you complete the full Universal AI program, you will receive a Program certificate.
Throughout the course, you'll complete short multiple-choice knowledge checks after videos or sections to confirm your understanding, plus broader multiple-choice assignments at the end of each module that cover the full range of material. Some assignments use coding notebooks, giving you hands-on practice interpreting AI outputs. However, you won't need to write any code — all code is provided, so you can focus on reading, running, and interpreting the results. There are no exams.
This course helps you understand the technology behind today's most powerful AI tools. You'll learn how to:
- Understand how modern AI "learns". You'll learn the basics of neural networks, the technology behind many AI systems. These skills will help you see how AI models make predictions in order to discuss, evaluate, and question AI tools at work.
- Spot common problems. You'll learn why AI models sometimes get things wrong, like memorizing examples instead of truly learning from them.
- Build a simple AI model yourself. You'll learn how AI systems are put together and be better equipped to evaluate AI tools.
- Design and evaluate models for real tasks. You'll work on classification and regression problems and learn how models handle both structured and unstructured data, which will help you understand what AI can and can't do for your business or field.
