Machine Learning:

From Data to Decisions

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MIT CEU’s

Machine Learning:

From Data to Decisions

Download the brochureRegister
START
END
DURATION
HOURS OF EFFORT
LANGUAGE
FORMAT
PRICE
MIT CEU’s

*

Machine Learning as competitive advantage

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. It is fast becoming a fundamental tool for making sound decisions by analyzing large quantities of data and events. Its objective: reducing spaces of uncertainty and arbitrariness through automatic learning and providing organizations and professionals the security needed to make impactful decisions.

80%

80% of business and technology leaders say that artificial intelligence is already pushing productivity in their workplaces.

Source: Narrative Science

61%

61% of marketing specialists say that artificial intelligence is the most important aspect of their data strategy.

Source: MeMSQL

40%

The technology behind today’s artificial intelligence can increase business productivity by up to 40%.

Source: Accenture

An online course to optimize decision-making

MIT Professional Education brings you closer to this new technology that is revolutionizing the global economy through an online course that guides professionals through the basics and the applications of Machine Learning. Participants will receive training in data analysis and comprehension, preparing them to fully master reliable, data-informed decision-making in their organizations.

DISCOVER THE COURSE’S CONTENT

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The skills you will develop

1.

Data comprehension.

In a hyper-connected world, we are surrounded by data. As it gets harder every day to understand the information we are receiving, our first step is learning to gather relevant data and—more importantly—to understand it.

2.

Predictions.

Through supervised learning and data classification you will be able to make predictions. Additionally, you will discover basic concepts in machine learning, such as “neural networks”.

3.

Decision-making.

You will be capable of making effective decisions, eliminating spaces of uncertainty and arbitrariness through data analysis.

4.

Causal inferance

By correctly applying and evaluating observed experiences using Machine Learning we can infer relevant conclusions to drive strategy.

In addition, you will receive a Certificate of Completion

All participants who successfully complete the course will receive an MIT Professional Education Certificate of Completion.

Students in the MIT Professional Education Digital Plus Program Cloud & DevOps: Continuous Transformation will also receive * Continuing Education Units (CEU*).

To obtain the CEUs, it is a requirement to complete a form necessary for the accreditation of the CEUs. These CEUs are calculated, for each course, according to the number of learning hours.

* A CEU is a unit of credit equivalent to 10 hours of participation in an accredited course for professionals.

This course is aimed at

  • CEOs, MANAGERS, AND OTHER EXECUTIVES in various industries who lead teams with technical responsibilities.
  • TECHNICAL PROFESSIONALS OR WITH TECHNICAL BACKGROUNDS that work with large amounts of data and want to take advantage of Machine Learning to improve decision-making processes.

Meet the instructors of this course

*Listed in alphabetical order 
PROF. DEVAVRAT SHAH

Professor in the department of Electrical Engineering and Computer Science at MIT

“How can leaders utilize data to gain a competitive advantage in their industries? What systematic steps can they take to use those data? Discover the answers to these questions with me in this course.”

Learn more

Devavrat Shah is Andrew (1956) and Erna Viterbi Professor with the department of Electrical Engineering and Computer Science at MIT. He is the founding director of Statistics and Data Science Center, Institute for Data, Systems and Society. He is a member of LIDS, CSAIL and ORC at MIT. Currently, he directs Deshpande Center for Technological Innovation at MIT.

His current research interest is in developing large-scale machine learning algorithms for unstructured data with particular interest in social data. He has made contributions to development of “gossip” protocols and “message-passing” algorithms for statistical inference which have been pillars of modern distributed data processing systems.

He co-founded Celect, Inc. which has been part of Nike since 2019. In 2019, he co-founded Ikigai Labs with the mission of building self-driving organizations by empowering data business operators to make data-driven decisions with ease of spreadsheets.

His work has been covered in popular press including NY Times, Forbes, and Wired, and has received broad recognition, including prize paper awards in Machine Learning, Operations Research and Computer Science, and career prizes including the 2010 Erlang prize from the INFORMS Applied Probability Society.

Discover the experience of our participants

SEE MORE

Are you ready to make reliable, data-informed decisions?

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