• Program Duration 4 months
  • Learning Format Self-Paced Learning

World’s #1 Online Bootcamp

9/10 of our learners achieve their learning objectives after successful course completion*

Process Advisorsimage

Why Join this Program

The MIT xPRO Impact

Earn a professional certificate and 10 Continuing Education Units (CEUs) from MIT xPRO

Learn From The Best

Gain insights through masterclasses from distinguished MIT faculty

Global Network

Interact with a global peer group while working on real-world projects

Applied Learning

Learn through simulations, assessments, case studies, and tools

FOR ENTERPRISE

Looking to enroll your employees into this program ?

Program Overview

Understand applications of machine learning in various industries and domains with this Machine Learning Engineering. Designed and delivered by MITxPro, this program brings a hands-on approach to understanding the computational tools used in engineering and business problem-solving.

Key Features

  • Professional certificate of completion from MIT xPRO
  • 10 Continuing Education Units (CEUs) from MIT xPRO
  • Live online masterclasses from distinguished MIT faculty
  • Connect with an international community of professionals
  • Work on real-world projects

Program Advantage

This Machine Learning for Business, Engineering, and Science Program leverage MIT's thought leadership in engineering, science, and management.

  • MIT xPRO Certificate

    About MIT xPRO:

    • Professional certificate of completion from MIT xPRO
    • 10 Continuing Education Units (CEUs) from MIT xPRO
    • Masterclasses from leading MIT faculty and industry experts

Program Details

Through a combination of simulations, assessments, case studies, and tools learn about applications of machine learning in various business, engineering, and physical sciences domains, and apply your knowledge to various aspects of your work.

Learning Path

  • Get started with this Machine Learning Engineering and explore everything about it.

  • This Machine Learning Engineering Course will help you understand the computational tools used in engineering and business problem-solving. This course covers the foundations - from modeling and simulation fundamentals to topics such as probability, and optimization to deeper concepts used in machine learning. This Machine Learning Program also takes you through the various tools/algorithms/methods used to analyze various use cases and help model the solution.

  • Learn how some of the computational tools used in engineering & business problem-solving are put into practice in this module. The course will take you through the practical applications of machine learning across various industries and domains through examples, case studies, and simulations. *Topics in this module could be covered through asynchronous mode/masterclasses and are subject to change at the discretion of MIT xPRO.

Electives:

Skills Covered

  • Machine learning applications
  • Optimization techniques
  • Simulations
  • Probability and statistics
  • Realworld forecasting
  • Risk assessment amp quantification
  • Predictions with missingsparse data
  • Engineering problemsolving

Program Advisors

  • Youssef. M. Marzouk

    Youssef. M. Marzouk

    Faculty Co-Director of MIT Center of Computational Engineering

    Youssef. M. Marzouk is the faculty Co-Director of the MIT Center of Computational Engineering, Associate Professor of Aeronautics & Astronautics, and Director of the Aerospace Computational Design Laboratory, at MIT

  • Laurent Demanet

    Laurent Demanet

    Professor of Applied Mathematics & Director of MIT's Earth Resources Laboratory

    Laurent Demanet is a Professor of Applied Mathematics, in the Department of Mathematics at MIT. He holds a joint appointment with the Department of Earth, Atmospheric, and Planetary Sciences, where he is the Director of MIT's Earth Resources Laboratory.

  • George Barbastathis

    George Barbastathis

    Professor of Mechanical Engineering

    George Barbastathis is a professor of Mechanical Engineering at MIT. He holds a Ph.D. from Caltech. Professor Barbastathis served as a visiting scholar for Harvard University and as a research scientist for Singapore-MIT Alliance for Research and Technology Centre.

  • Justin Solomon

    Justin Solomon

    Associate Professor of Electrical Engineering and Computer Science

    Justin Solomon is an Assistant Professor in the Department of Electrical Engineering and Computer Science at MIT. He received a Ph.D. in computer science from Stanford University Before his graduate studies and was a member of Pixar's Tools Research group.

  • Richard Braatz

    Richard Braatz

    Edwin R. Gilliland Professor of Chemical Engineering

    Richard Braatz is an Edwin R. Gilliland Professor of Chemical Engineering, MIT. Professor Braatz holds a Ph.D. and M.S from Caltech. His key interests include applied mathematics and control theory, data analytics and machine learning, and materials processing.

  • Markus Buehler

    Markus Buehler

    Professor of Engineering & Head, Dpt. of Civil & Environmental Engineering

    McAfee Professor of Engineering & Head of the Department of Civil & Environmental Engineering at MIT. Professor Buehler holds a Postdoctoral Scholar, the Division of Chemistry and Chemical Engineering, Caltech, and a P.h.D in Materials Science from the University of Stuttgart.

  • Themistoklis Sapsis

    Themistoklis Sapsis

    Associate Professor of Mechanical & Ocean Engineering

    Themistoklis Sapsis is an Associate Professor of Mechanical & Ocean Engineering at MIT. His interests include the quantification and prediction of extremes events in complex systems and targeted energy transfers in mechanical systems

  • John Williams

    John Williams

    Professor Civil & Environmental Engineering

    John R. Williams is a Professor of Information Engineering and Civil and Environmental Engineering at MIT. Professor Williams holds a BA in physics from Oxford University, an M.Sc. in physics from UCLA, and a Ph.D. from Swansea University.

  • Heather Kulik

    Heather Kulik

    Associate Professor of Chemical Engineering

    Heather Kulik is an Associate Professor of Chemical Engineering, at MIT. Professor’s research interests focus on catalysis, transition-metal chemistry, electronic structure methods, atomistic simulations, and enzyme catalysis. She has achieved many honors and awards.

prevNext

Batch Profile

This program caters to working professionals from a variety of industries and backgrounds; the diversity of our students adds richness to class discussions and interactions.

  • The class consists of learners from excellent organizations and diverse industries
    Industry
    Software Product - 50%IT - 50%
    Companies
    Cognizant
    Wipro
    American Express
    Genpact

Learner Reviews

Admission Details

Application Process

The application process is organized and led by Simplilearn and consists of 3 simple steps. An admission offer will be made to selected candidates and accepted by paying the fee.

STEP 1

Submit Application

Complete the application process by submitting the required documents

STEP 2

Application Review

An admissions panel will shortlist candidates based on their application

STEP 3

Admission

Selected candidates can access the program from the batch start date

Eligibility Criteria

Program is best suited for:

Individuals with a background of college-level math
Professionals looking to gain insights into ML applications
Some experience with MATLAB (R) is helpful

Admission Fee & Financing

The admission fee for this program is $ 2,375

Financing Options

We are dedicated to making our programs accessible. We are committed to helping you find a way to budget for this program and offer a variety of financing options to make it more economical.

Pay in Installments

You can pay monthly installments for Post Graduate Programs using Splitit, Affirm or ClimbCredit payment option with low APR and no hidden fees.

AffirmClimbCredit

Other Financing Options

We provide the following options for one-time payment

  • Credit Card
  • Paypal

$ 2,375

Apply Now

Program Benefits

  • Rigorous curriculum designed and delivered by MIT faculty
  • Live online masterclasses from MIT
  • 5 Continuing Education Units (CEUs) from MIT xPRO
  • Connect with an international community of professionals
  • Rigorous curriculum designed by MIT faculty

Program Cohorts

There are no cohorts available in your region currently

Got questions regarding upcoming cohort dates?

FAQs

  • Data Sharing Policy

    Data Sharing Policy

    MIT xPRO is collaborating with online education provider Simplilearn to deliver this Machine Learning Engineering Course through a dynamic, interactive, digital learning platform. This course leverages MIT xPRO's thought leadership in engineering and management practice developed over years of research, teaching, and practice. Accessibility

    CONSENT TO RELEASE LEARNER INFORMATION

     

    I hereby authorize the Massachusetts Institute of Technology ("MIT") to release

    identified information relating to me to Simplilearn, as described below:

     

    Information to be released:

    • Learner data including course progress and completion

    • Demographic information from the learner profile including gender, year of birth, and level of education

    • Learner data collected through survey instruments and the online discussions boards

  • Who is this Machine Learning program best suited for?

    This program is best suited for:

    • Professionals looking to understand the applications of machine learning across various engineering, science, and business fields 

    • Professionals with a bachelor's degree in engineering (e.g., mechanical, civil, aerospace, chemical, materials, nuclear, biological, electrical, etc.), business, or physical sciences 

    • Professionals with a background in college-level mathematics including differential calculus, linear algebra, and statistics 

    • Programming experience is not necessary, but some experience with MATLAB (R) is very useful

  • Will financial aid be provided for this Machine Learning for Business, Engineering, and Science program?

    To ensure money is not a limiting factor in learning, we offer various financing options to help make this program financially manageable. For more details, please refer to our "Admissions Fee and Financing" section

  • What should I expect from this Machine Learning for Business, Engineering and Science program?

    As a part of this machine learning engineering program, you will receive the following:

    • Professional certificate of completion from MIT xPRO

    • 10 Continuing Education Units (CEUs) from MIT

    • Live online masterclasses from distinguished MIT faculty

    • Connect with an international community of professionals 

    • Work on real-world projects

  • What certificate will I receive?

    MIT xPRO is collaborating with online education provider Simplilearn to deliver this online program. This course leverages MIT xPRO's thought leadership in machine learning engineering, science, and management developed over years of research, teaching, and practice. Upon completion of this program, you will be awarded a certificate from MIT xPRO.

  • Disclaimer
  • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.
  • *According to Simplilearn survey conducted and subject to terms & conditions with Ernst & Young LLP (EY) as Process Advisors