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Data Science for Business Leaders: ML Fundamentals
Highest Rated
Rating: 4.5 out of 5(1,348 ratings)
10,167 students

Data Science for Business Leaders: ML Fundamentals

A no-code introduction for leaders to understanding machine learning (and AI) as a business capability.
Created byRobert Fox
Last updated 7/2022
English

What you'll learn

  • 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 ("AI", "deep learning", etc.); plain and simple explanations (without the hype).
  • Fair warning: NO hands-on model development (NO code & NO complex formulas)
  • Includes sections dedicated to *identifying* and *quantifying* machine learning opportunities.
  • Focused on understanding ML as a capability that can benefit any business.

Course content

6 sections87 lectures8h 42m total length
  • About this Course: Machine Learning Fundamentals5:58
  • What is Covered in this Course, Learning Support, & Discounts2:54
  • Brief Instructor Bio3:02

Requirements

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

Description

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.  Whether your are building or buying "AI-powered" solutions, you should consider how your organization 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.

This course is broken into 5 key parts:

  • Part 1: Models, Machine Learning, Deep Learning, & Artificial Intelligence Defined

    • This part has a simple mission: to give you a solid understanding of what Machine Learning is.  Mastering the concepts and the terminology is your first step to leveraging them as a capability.  We walk through basic examples to solidify understanding.

  • Part 2: Identifying Use Cases

    • Tired of hearing about the same 5 uses for machine learning over and over?  Not sure if ML even applies to you?  Take some expert advice on how you can discover ML opportunities in *your* organization. 

  • Part 3: Qualifying Use Cases

    • Once you've identified a use for ML, you'll need to measure and qualify that opportunity.  How do you analyze and quantify the advantage of an ML-driven solution?  You do not need to be a data scientist to benefit from this discussion on measurement.  Essential knowledge for business leaders who are responsible for optimizing a business process.

  • Part 4: Building an ML Competency

    • Key considerations and tips on building / buying ML and AI solutions.

  • Part 5: Strategic Take-aways

    • A view on how ML changes the landscape over the long term; and discussion of things you can do *now* to ensure your organization is ready to take advantage of machine learning in the future.


Who this course is for:

  • Business leaders, executives, product managers, process owners, service managers, or anyone who is responsible for how their organization operates.
  • Suitable for people with both technical and non-technical backgrounds.
  • Can be a helpful business-point-of-view for aspiring and experienced data scientists.