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Machine Learning with Python: From Basics to Deep Learning
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Machine Learning with Python: From Basics to Deep Learning

This course is part of multiple programs. Learn more.

Course Cost

₹ 25,372

Advanced

Skill Level

15 Weeks

Live Classes lessons

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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4.1

2,90,511 Enrolled

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Regina Barzilay

Regina Barzilay

Tommi Jaakkola

Tommi Jaakkola

English

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olive-leaves-logo

4.1

2,90,511 Enrolled

olive-leaves-logo

English

Regina Barzilay

Regina Barzilay

Tommi Jaakkola

Tommi Jaakkola

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

  • Understand reinforcement learning applications

  • Design effective feature engineering strategies

Skills you'll gain

machine learning
python programming
deep learning
neural networks
data science
algorithms
reinforcement learning
statistical modeling

This course includes:

PreRecorded video

Projects, Assignments, Exams

Access on Mobile, Tablet, Desktop

Limited Access access

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Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.

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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

Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Statistics and Data Science, Statistics and Data Science in Social Sciences, Statistics and Data Science with Time Series, Statistics and Data Science Methods

Reviews

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Faculties

These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.

Regina Barzilay
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21

Courses

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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.
Tommi Jaakkola
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21

Courses

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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.
Karene Chu
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25

Courses

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Karene Chu serves as the Assistant Director of Education and Research Scientist at MIT's Institute for Data, Systems, and Society, where she has made significant contributions to digital learning initiatives. After receiving her Ph.D. in mathematics from the University of Toronto in 2012, she completed postdoctoral fellowships at both the University of Toronto/Fields Institute and MIT, specializing in knot theory and quantum invariants. In 2015, she transitioned to become a digital learning lab fellow at MIT, where she has since played a pivotal role in developing and managing the MicroMasters Program in Statistics and Data Science. Her educational impact includes co-authoring the MITx Calculus Series, which became a Top 10 edX course in 2016, and leading the development of a five-course series on differential equations. She is also a key instructor in MIT's Machine Learning with Python course alongside Regina Barzilay and Tommi Jaakkola. Her teaching excellence was first recognized at the University of Toronto, where she received a teaching award for her work in single and multi-variable calculus and linear algebra. As part of MIT's edX group, she collaborated with colleagues to earn the inaugural MITx Prize for Teaching and Learning in MOOCs, demonstrating her commitment to advancing digital education and making complex mathematical concepts accessible to learners worldwide.
Machine Learning with Python: From Basics to Deep Learning

Advanced

Skill Level

15 Weeks

Live Classes lessons

Course Cost

₹ 25,372

Completion

CERTIFICATE

Frequently asked Questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.