Machine Learning with Python: from Linear Models to Deep Learning

MOOC
Machine Learning with Python: from Linear Models to Deep Learning
Language
English
Duration
3 months
Certificate
Certification paid
Course by EdX
Machine Learning with Python: from Linear Models to Deep Learning
What will you learn?
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.
About the course

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/.

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

Program
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. — Course 4 of 4 in the MITx MicroMasters program in Statistics and Data Science.
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. — Course 4 of 4 in the MITx MicroMasters program in Statistics and Data Science.
Lecturers
Regina Barzilay
Regina Barzilay
Delta Electronics Professor in the Department of Electrical Engineering and Computer Science MIT
Tommi Jaakkola
Tommi Jaakkola
Thomas Siebel Professor of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society MIT
Karene Chu
Karene Chu
Lecturer and Research Scientist Massachusetts Institute of Technology
Platform
/storage/img/providers/edx.svg
All the courses on this platform are free of charge. The authors are top universities and corporations that seek to maintain high quality standards. If you do not meet a deadline for assignments, you lose points. Like on other platforms, the videos in which the theory is explained are followed by practical assignments. Courses are available in English, Chinese, Spanish, French and Hindi.
Like any other website, konevy uses «cookies». These cookies are used to store information including visitor's preferences, and the pages on the website that the visitor accessed or visited. The information is used to optimize the users' experience by customizing our web page content based on visitors' browser type and/or other information. For more general information on cookies, please read the «What Are Cookies» article on Cookie Consent website.