Data Science for Business Leaders: Machine Learning Defined

Understanding Machine Learning as a Business Capability
Rating: 4.3 out of 5 (43 ratings)
3,960 students
English
English [Auto]
Learn what models are, how they work, and how they fit in the overall picture of machine learning (ML) and data science.
Lots of terminology; plain and simple explanations (without the hype).
Fair warning: NO hands-on model development (NO code & NO complex formulas)
Focused on understanding ML as a capability that can benefit any business.

Requirements

  • No prior knowledge required.
  • This course has no coding or complex mathematics.
  • This class is the prerequisite for many other data science courses.

Description

This course has a simple mission: to give you a solid understanding of what Machine Learning is.  Mastering the terminology is your first step to understanding the ideas and capabilities. 

Machine learning is a capability that business leaders should grasp if they want to extract value from data.   There's a lot of hype; but there's some truth: the use of modern data science techniques could translate to a leap forward in progress or a significant competitive advantage.  Often you can buy "AI-powered" solutions; but you should consider how your organization's core competencies could benefit from machine learning. 

No coding or complex math. This is not a hands-on course. We set out to explain all of the fundamental concepts you'll need in plain English.


Who this course is for:

  • Business leaders, executives, product managers, process owners, service managers, as well as aspiring and experienced data scientists.
  • Suitable for people with both technical and non-technical backgrounds.

Course content

4 sections11 lectures1h 57m total length
  • Welcome
    09:27
  • What is a Model and Why is it Important?
    05:47

Instructor

Data Scientist, CIO
Robert Fox
  • 4.3 Instructor Rating
  • 43 Reviews
  • 3,960 Students
  • 1 Course

Robert has been mucking around with data for over 20 years.  Much of his career was spent in financial services, where he climbed to SVP - Data Science & Analytics at Bank of America (head of data science for merchant services).  Recently he did a stint at DataRobot, helping customers understand and use automated machine learning.  And now he's working as a fractional CIO and training and researching under his own company: Decision Science.