Machine Learning

Improve your organization with a business focus on machine learning applications

Duration

6 weeks, excluding
orientation

Effort

7-10 hours per week,
entirely online

Learning Format

Weekly modules,
flexible learning

This Course Is for You If:

  • You’re interested in deploying machine learning applications to solve business problems in your organization.
  • You want to develop and promote a strategy for implementing machine learning in order to achieve data maturity.
  • You’d like to bridge the gap between technical- and business-oriented teams through communication and collaborative partnerships.

Course Curriculum

Over the duration of this online short course, you’ll work your way through the following modules:

Module 1:
Solving Business Problems With Machine Learning

Use machine learning to solve problems in an organization.

Module 2:
Ethics and Due Process

Explore the ethical implications of using data to solve a business problem.

Module 3:
Measuring and Evaluating Model Performance

Determine the performance of machine learning models.

Module 4:
Deep Learning for Business

Consider the appropriate applications of deep learning in business.

Module 5:
Model Productionalization

Examine the factors affecting a model’s readiness for deployment.

Module 6:
Justifying an Organizational Machine Learning Approach

Communicate a machine learning approach to a wider audience.

 

Prerequisites

This course recognizes that deploying machine learning in an organization requires practical operational insight into the technology and the ability to foster collaboration between teams.
The course does not require technical abilities, and does not teach you to code — rather, it aims to equip you with the ability to solve problems in your business using machine learning, in order to help your organization achieve data maturity.

Upon Completion of This Course, You'll Have:

1

An understanding of the capabilities of machine learning (ML), and the knowledge to formulate your business problem to solve it effectively.

2

An effective process for deploying, monitoring and evaluating the ML model, as well as assessing its relevance, and the uses of different ML models.

3

The basis of a foundation of knowledge to collect, process and utilize data efficiently — ethically and securely — to achieve organizational data maturity.

4

The ability to communicate the business case for your ML approach, and guide your coding and ML efforts in the right direction.

Your Faculty Director

D. Alex Hughes

Faculty Director, Assistant Adjunct Professor at the UC Berkeley School of Information (I School)

Professor D. Alex Hughes's research and teaching focuses on using experiments to generate credible causal statements. His research has been published in The Lancet, The Proceedings of the National Academies of Science, Perspectives on Politics, and various field journals. He has collaborated closely with machine learning teams across a range of industries and machine learning maturity — from Fortune 100 companies to start-ups. Alex has been honored repeatedly with the I School’s Distinguished Faculty Award, which is awarded to one outstanding teaching faculty member each year.

An Engaging Online Education That Sets You Apart

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Gain verifiable and relevant competencies and earn invaluable recognition from an international university, entirely online and in your own time

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Enjoy a personalized online learning experience, with feedback on weekly assignments from industry experts, created to make you feel supported at every step

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Experience a flexible but structured approach to online education as you plan your learning around your schedule to meet weekly milestones

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