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Machine Learning with Python: From Basics to Deep Learning
Master machine learning fundamentals through hands-on Python projects in this comprehensive MIT course covering linear models to deep learning.
Master machine learning fundamentals through hands-on Python projects in this comprehensive MIT course covering linear models to deep learning.
This advanced MIT course provides a comprehensive introduction to machine learning, covering theoretical foundations and practical implementations. Students learn about classification, regression, clustering, and reinforcement learning through hands-on Python projects. The curriculum spans from basic linear models to advanced topics like neural networks, deep learning, and probabilistic modeling. Designed for technical professionals, the course emphasizes both theoretical understanding and practical application through real-world projects in Python.
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What you'll learn
Master core machine learning principles and algorithms
Implement and analyze various predictive models
Develop neural networks and deep learning systems
Apply machine learning to real-world problems
Optimize model performance through parameter tuning
Build end-to-end machine learning projects in Python
Skills you'll gain
This course includes:
PreRecorded video
Projects, Assignments, Exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 17 modules in this course
This comprehensive machine learning course covers fundamental concepts through advanced applications. Students explore linear classifiers, neural networks, deep learning, and reinforcement learning. The curriculum combines theoretical principles with practical implementation through Python projects. Topics include classification, regression, clustering, feature engineering, and model optimization. Three major projects provide hands-on experience in review analysis, digit recognition, and reinforcement learning applications.
Introduction
Module 1
Linear classifiers, separability, perceptron algorithm
Module 2
Maximum margin hyperplane, loss, regularization
Module 3
Stochastic gradient descent, over-fitting, generalization
Module 4
Linear regression
Module 5
Recommender problems, collaborative filtering
Module 6
Non-linear classification, kernels
Module 7
Learning features, Neural networks
Module 8
Deep learning, back propagation
Module 9
Recurrent neural networks
Module 10
Generalization, complexity, VC-dimension
Module 11
Unsupervised learning: clustering
Module 12
Generative models, mixtures
Module 13
Mixtures and the EM algorithm
Module 14
Learning to control: Reinforcement learning
Module 15
Reinforcement learning continued
Module 16
Applications: Natural Language Processing
Module 17
Fee Structure
Instructors
21 Courses
Distinguished MIT Professor Pioneering AI Applications in Healthcare
Regina Barzilay, born in 1970 in Chișinău, Moldova, currently serves as the School of Engineering Distinguished Professor for AI and Health at MIT and the AI faculty lead at the MIT Jameel Clinic. After emigrating to Israel at age 20, she completed her education at Ben-Gurion University before earning her Ph.D. from Columbia University in 2003. Her groundbreaking research spans machine learning, drug discovery, and clinical AI, with particular focus on developing AI models for healthcare applications. Her personal experience with breast cancer in 2014 motivated her to direct her expertise toward oncology research, leading to significant breakthroughs in early cancer detection and drug development. Her notable achievements include developing machine learning models for early breast cancer diagnosis and the discovery of novel antibiotics. Her exceptional contributions have earned her numerous prestigious honors, including the 2017 MacArthur "Genius Grant," the 2020 AAAI Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity (with a $1 million prize), and election to both the National Academy of Engineering and National Academy of Medicine in 2023. Most recently, she was awarded the 2025 IEEE Frances E. Allen Medal for her innovative machine learning algorithms that have advanced human language technology and impacted medicine.
21 Courses
Pioneer in Machine Learning Theory and Applications
Tommi S. Jaakkola serves as the Thomas Siebel Professor at MIT, holding joint appointments in Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society. After completing his M.Sc. in theoretical physics from Helsinki University of Technology in 1992 and Ph.D. in computational neuroscience from MIT in 1997, he briefly held a postdoctoral position in computational molecular biology at UCSC before joining the MIT faculty in 1998. His research spans foundational machine learning theory to practical applications, with particular focus on statistical inference and estimation tasks. His current work includes developing generative AI models for molecular sciences, automated drug design, and creating self-explaining models for transparent AI. His research group advances how machines can learn, predict, and control at scale in an efficient, principled, and interpretable manner. They develop innovative methods for machine learning that emphasize efficiency, scalability, and interpretability, particularly in areas such as drug design, biomedical applications, and strategic game-theoretic interactions. His exceptional contributions have been recognized with numerous honors, including the AISTATS Test of Time Award in 2022, the Jamieson Award for Excellence in Teaching in 2015, and election as an AAAI Fellow. Under his leadership, MIT's machine learning courses have grown significantly, with the undergraduate course now enrolling more than 500 students per term.
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