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Machine Learning with Python: from Linear Models to Deep Learning

An in-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python projects. -- Part of the MITx MicroMasters program in Statistics and Data Science.

...
Machine Learning with Python: from Linear Models to Deep Learning

There is one session available:

200,609 already enrolled!
Starts Sep 4
Ends Dec 19
Enroll

Machine Learning with Python: from Linear Models to Deep Learning

An in-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python projects. -- Part of the MITx MicroMasters program in Statistics and Data Science.

Machine Learning with Python: from Linear Models to Deep Learning
15 weeks
10–14 hours per week
Instructor-paced
Instructor-led on a course schedule
Free
Optional upgrade available

There is one session available:

200,609 already enrolled! After a course session ends, it will be archivedOpens in a new tab.
Starts Sep 4
Ends Dec 19
Enroll
Machine Learning with Python: from Linear Models to Deep Learning

About this course

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If you have specific questions about this course, please contact us atsds-mm@mit.edu.

Machine learning methods are commonly used across engineering and sciences, from computer systems to physics. Moreover, commercial sites such as search engines, recommender systems (e.g., Netflix, Amazon), advertisers, and financial institutions employ machine learning algorithms for content recommendation, predicting customer behavior, compliance, or risk.

As a discipline, machine learning tries to design and understand computer programs that learn from experience for the purpose of prediction or control.

In this course, students will learn about principles and algorithms for turning training data into effective automated predictions. We will cover:

  • Representation, over-fitting, regularization, generalization, VC dimension;
  • Clustering, classification, recommender problems, probabilistic modeling, reinforcement learning;
  • On-line algorithms, support vector machines, and neural networks/deep learning.

Students will implement and experiment with the algorithms in several Python projects designed for different practical applications.

This course is part of theMITx MicroMasters Program in Statistics and Data Science. Master the skills needed to be an informed and effective practitioner of data science. You will complete this course and three others from MITx, at a similar pace and level of rigor as an on-campus course at MIT, and then take a virtually-proctored exam to earn your MicroMasters, an academic credential that will demonstrate your proficiency in data science or accelerate your path towards an MIT PhD or a Master's at other universities. To learn more about this program, please visit https://micromasters.mit.edu/ds/.

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At a glance

  • Language: English
  • Video Transcript: English
  • Associated skills: Support Vector Machine, Experimentation, Physics, Artificial Neural Networks, Statistics, Prediction, Reinforcement Learning, Python (Programming Language), Machine Learning Algorithms, Consumer Behaviour, Sales, Deep Learning, Machine Learning, Forecasting, Recommender Systems, Algorithms, Linear Model, Data Science

What you'll learn

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  • Understand principles behind machine learning problems such as classification, regression, clustering, and reinforcement learning
  • Implement and analyze models such as linear models, kernel machines, neural networks, and graphical models
  • Choose suitable models for different applications
  • Implement and organize machine learning projects, from training, validation, parameter tuning, to feature engineering.

Syllabus

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Lectures :

  • Introduction
  • Linear classifiers, separability, perceptron algorithm
  • Maximum margin hyperplane, loss, regularization
  • Stochastic gradient descent, over-fitting, generalization
  • Linear regression
  • Recommender problems, collaborative filtering
  • Non-linear classification, kernels
  • Learning features, Neural networks
  • Deep learning, back propagation
  • Recurrent neural networks
  • Generalization, complexity, VC-dimension
  • Unsupervised learning: clustering
  • Generative models, mixtures
  • Mixtures and the EM algorithm
  • Learning to control: Reinforcement learning
  • Reinforcement learning continued
  • Applications: Natural Language Processing

Projects :

  • Automatic Review Analyzer
  • Digit Recognition with Neural Networks
  • Reinforcement Learning

About the instructors

Frequently Asked Questions

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Should you have further inquiries, please go to https://micromasters.mit.edu/ds/ and use the "Contact us" button.

Who can take this course?

Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.

This course is part of Statistics and Data Science (General track) MicroMasters Program

Learn more 
Expert instruction
5 graduate-level courses
Instructor-led
Assignments and exams have specific due dates
1 year 5 months
10 - 14 hours per week

Ways to take this course

Choose your path when you enroll.
Verified Track
Audit Track

Price

$300 USD

Free

Access to course materials

Unlimited

Limited

Expires on Dec 18

World class institutions and universities

edX support

Shareable certificate upon completion

Graded assignments and exams

Read our FAQsin a new tab about frequently asked questions on these tracks.

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