Data Science for Business Leaders: ML Fundamentals

A no-code introduction for leaders to understanding machine learning (and AI) as a business capability.

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.

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.