Data Science for Business Leaders: ML Fundamentals
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.
- No prior knowledge required.
- This course has no coding or complex mathematics.
- This class is the prerequisite for other data science courses.
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.
Robert has been in the field that is now called Data Science for over 20 years. Much of his career was spent in financial services, where he held many roles, most recently SVP - Data Science & Analytics at Bank of America's joint venture with First Data. Recently he was a consultant and teacher with 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.