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ONLINE COURSE

Machine Learning:

From Data to Decisions

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Why enroll in this course?

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Propel your organization into the future with intelligent predictive analyses, methods, and toolkits.

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Automate decision-making processes within your organization to mitigate risks and yield optimal results.

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Discover holistic data comprehension to extrapolate relevant conclusions and drive organizational strategy.

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Engage in two live sessions with MIT instructors, and up to eight live sessions with learning facilitators, industry experts, and peers.

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Networking opportunities establish professional connections with industry experts and your cohort.

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Access to rich supplementary resources provides additional materials and content for a more thorough educational journey.

Certificate

Certificate

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.

Course outline

Machine learning is a tool being used by business managers from all sectors to make data-based decisions. During this online program, participants will learn the four-step process of machine learning, from analyzing data to evaluating the effectiveness of decisions made based on that data. Upon completion, participants will be able to leverage machine learning to improve and modernize their organization.

  • What is Machine Learning?

  • Differences Between Machine Learning, Statistics, and Artificial Intelligence.

  • The Building Blocks of Machine Learning.

  • Understanding Data.

  • Prediction, Decision-Making, & Causal Inference.

  • Ask the right questions to understand the data.

  • Know what tools to use to unlock knowledge.

  • Understand how data visualization makes data clearer.

  • How to build a model that best fits the data.

  • How to quantify the degree of uncertainty.

  • What do you do when you don’t have enough data?

  • What lies beyond linear regression?

  • Compare different methods’ ability to minimize prediction errors.

  • Make better predictions based on your data and the desired outcome.

  • Use the right approaches to deal with data complexity.

  • What are neural networks and how they work.

  • Explore the history of neural networks and see examples of complex neural network systems.

  • How neural networks minimize errors, regardless of the size of the data set.

  • Learn how the decisions you make impact the immediate future and beyond.

  • Choose the right approach based on the environment, the rate of information flow, and your goal.

  • Find the balance between exploration (identifying what we don’t yet know) and exploitation (using what we already know).

  • Learn how to make real-time recommendations for clients to get more business.

  • Learn how to achieve optimal inventory management.

  • See how data scientists are exploring ways to predict the future price of digital goods like Bitcoin.

  • Given a set of observations, identify what caused those observations.

  • Learn to design experiments that provide insightful conclusions.

Who is this online course for?

  • Technical professionals with responsibility who want to take advantage of machine learning to improve decision-making processes.

  • CEOs, managers, and other nontechnical executives from all sectors who manage teams with responsibilities at the technical level.

  • Technical professionals seeking to acquire the necessary knowledge base for machine learning.

Instructor

MPE - Faculty - Devavrat Shah
Prof. Devavrat Shah

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

MIT Professional Education in Numbers

+60K

Participants in our courses

+155

Countries represented by our participants

92%

Rate the experience as extraordinary

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