Data Science for Business Leaders: ML Fundamentals

Data Science for Business Leaders: ML Fundamentals udemy course free download

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

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.