Reinforce Your Team's Performance

Transform your department or organization with MIT Professional Education group benefits

The more team members you enroll in your organization, the more benefits you can acquire. Depending on the number of members enrolled in our courses, you could obtain these benefits:

  • Special pricing
  • Focus-based, industry-specific feedback from specialized learning facilitators
  • Collaborative learning experience
  • Overall better performance and outcomes

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Why Enroll in this Course?

Push your organization forward with intelligent predictive analyses, methods, and toolkits.

Automate Decision Making in your organization to evade risks and make the best decisions possible.

Discover holistic data comprehension to infer relevant conclusions and drive strategy.

Decision-making dictates every organization's direction and development. Those responsible for making those decisions must be empowered to do so confidently using tools and data that eliminate chance and assure success. Machine Learning, a branch of Artificial Intelligence, has been created to help answer this need.

What will you learn?

Data comprehension

Predictions through supervised learning and data classification

Decision-making through data analysis

Causal inference with Machine Learning

Certificate Machine Learning: From Data to Decisions | MIT Professional Education

All the participants who successfully complete their program will receive an MIT Professional Education Certificate of Completion, as well as Continuing Education Units (CEUs)*.

To obtain CEUs, complete the accreditation confirmation, which is available at the end of the course. CEUs are calculated for each course based on the number of learning hours.

*The Continuing Education Unit (CEU) is defined as 10 contact hours of ongoing learning to indicate the amount of time they have devoted to a non-credit/non-degree professional development program.

To understand whether or not these CEUs may be applied toward professional certification, licensing requirements, or other required training or continuing education hours, please consult your training department or licensing authority directly.

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Course Outline

Module 1: Introduction to Machine Learning

We will start by addressing basic concepts such as black box, multidimensional data, prediction, and clustering, to begin to familiarize ourselves with the subject of study.

Module 2: Understanding Data

We will continue with the key characteristics of data sets and begin to identify statistical tools as well as effective visualization ways to extract information from the data.

Module 3: PREDICTIVE MODELS | Regression

In this module, for the first time we will deal with forecasting techniques, what are linear regressions and what are their limitations. We will develop complex skills to overcome obstacles that arise.

Module 4: PREDICTIVE MODELS | Classification

This would be the second block dedicated to prediction, we will learn to make classification models and we will study four methods to make them, so that we can choose what best suits the specific needs of each case.

Module 5: PREDICTIVE MODELS | Neural Networks

This will be the last block dedicated to prediction. We will deal with deep learning and its historical development, as well as the techniques and tools to train neural networks and specific applications.

Module 6: Basics of Decision-Making

At this point, we welcome you to the first block dedicated to decision making. We will analyze different frameworks and models that can be applied when making decisions to select the best option that adapts to different environments.

Module 7: Applications of Decision-Making

This is the second module applied to decision making. We already know the concepts and models, so we will address, as a reference, the processes that belong to the financial field. We will learn how to make recommendations to clients to boost business.

Module 8: Causal Inference

To finish the Machine Learning course, we will do experiments in order to analyze and understand the cause-and-effect relationships in the data sequences, which will allow us to make predictions of time series.



Professor in the department of electrical engineering and computer science at MIT

I firmly believe that applying a Machine Learning strategy in organizations is something very necessary in the present, as it allows us to make decisions in an optimized way, and therefore reduces possible strategy errors. Technology and data access give us an opportunity to be able to do this, so we should take maximum advantage of it. This program has assisted me in discovering Machine Learning, understanding data, exploring decision making, and evaluating its effectiveness, knowledge which I will apply in my professional development.

Sonsoles Catalá - Digital Marketing & Communications Specialist, Grupo Municipal Popular

Professionals from these leading organizations have completed this course:

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Logo Renault Nissan Mitsubishi
Logo Amazon
Logo Boeing
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Logo Google
Logo Johnson&Johnson
Logo PayPal
Logo Renault Nissan Mitsubishi