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Lead Instructor(s)
May 11 - Jul 13, 2022
Course Length
8 Weeks
Course Fee
6.4 CEUs
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Machine learning models, methods, and algorithms are helping leaders across industries make better decisions backed by data, rather than by feelings or guesswork. In this hands-on 8-week program, you’ll learn the most practical applications of machine learning, and explore a variety of relevant case studies and methods. There are no prerequisites for this course, though knowledge of basic statistics is helpful.

Course Overview

Today, every business has access to reams of data, whether it’s operational data, customer data, third party data, or supplier data. It’s a common challenge for organizations: how do we make optimal choices with so many unknown variables? It turns out that insights come from turning what is unknown into what is known.

This online course will help decisions makers leverage machine learning tools and techniques that facilitate that process and deliver tremendous impact to their projects. Machine learning is having profound effects in many different industries, from financial services to retail to advertising. It is fast becoming a fundamental tool for making better decisions in business—decisions driven by data, not gut feelings or guesswork.

This online program takes a look at machine learning through a lens of practical applications. It is designed specifically for professionals who want to develop a competitive edge by turning what is unknown into what’s known—leading to better decisions and outcomes. By the end of this course, you will be able to use your data to make informed predictions, take action, and evaluate the outcomes for future decision making.

Note: This online program requires no prerequisites in terms of math or computational sciences, although some experience with introductory-level statistics is helpful.

Participant Takeaways

  • Gain an understanding of the building blocks of machine learning
  • Understand your data in order to make more informed predictions by leveraging tools and techniques such as regression, classification, and neural networks
  • Build the foundations and understand the applications necessary to make critical decisions under uncertainty
  • Determine causal inferences to analyze the direct effects of different variables

Who Should Attend

Participants will gain a practical understanding of the tools and techniques used in machine learning applications. In the MIT tradition, you will learn by doing. There are no prerequisites in terms of math or computational science, although basic understanding of statistics is helpful. This is not a coding course, but rather an introduction to the many ways that machine learning tools and techniques can help make better decisions in a variety of situations.

Representative functions and industries of past participants include:

  • Chief Engineer, Manufacturing
  • Technical Lead, IT Products
  • VP Sales, Retail
  • Consultant, IT Services
  • Data Scientist, Media
  • Valuation Manager, Financial Services
  • Director, Business Intelligence
  • Software Developer, Technology
  • General Manager, Manufacturing
  • Associate Director, Healthcare

Program Outline

Module 1: Introduction and Overview of Machine Learning

Module 2: Understanding Your Data

Module 3: Prediction Part 1 - Regression

Module 4: Prediction Part 2 - Classification

Module 5: Prediction Part 3 - Neural Networks

Module 6: Decision Making Foundations

Module 7: Decision Making Applications

Module 8: Causal Inference