And for that reason, data scientists aren't the only ones who need to learn the fundamentals. Executives, decision makers, and line of business managers must also ramp up on how machine learning works and how it delivers business value.
And the reverse is true as well: Techies need to look beyond the number crunching itself and become deeply familiar with the business demands of machine learning. This way, both sides speak the same language and can collaborate effectively.
This course will prepare you to participate in the deployment of machine learning – whether you'll do so in the role of enterprise leader or quant. In order to serve both types, this course goes further than typical machine learning courses, which cover only the technical foundations and core quantitative techniques. This curriculum uniquely integrates both sides – both the business and tech know-how – that are essential for deploying machine learning. It covers:
– How launching machine learning – aka predictive analytics – improves marketing, financial services, fraud detection, and many other business operations
– A concrete yet accessible guide to predictive modeling methods, delving most deeply into decision trees
– Reporting on the predictive performance of machine learning and the profit it generates
– What your data needs to look like before applying machine learning
– Avoiding the hype and false promises of “artificial intelligence”
– The social justice concerns, such as when predictive models blatantly discriminate by protected class
No hands-on and no heavy math. This concentrated entry-level program is totally accessible to business leaders – and yet totally vital to data scientists who want to secure their business relevance. It's for anyone who wishes to participate in the commercial deployment of machine learning, no matter whether you'll play a role on the business side or the technical side. This includes business professionals and decision makers of all kinds, such as executives, directors, line of business managers, and consultants – as well as data scientists.
But Technical learners should take another look. Before jumping straight into the hands-on, as quants are inclined to do, consider one thing: This curriculum provides complementary know-how that all great techies also need to master. It contextualizes the core technology, guiding you on the end-to-end process required to successfully deploy a predictive model so that it delivers a business impact.
Like a university course. This course is also a good fit for college students, or for those planning for or currently enrolled in an MBA program. The breadth and depth of the overall three-course specialization is equivalent to one full-semester MBA or graduate-level course.
In- depth yet accessible. Brought to you by industry leader Eric Siegel – a winner of teaching awards when he was a professor at Columbia University – this curriculum stands out as one of the most thorough, engaging, and surprisingly accessible on the subject of machine learning.
Vendor-neutral. This course includes illuminating software demos of machine learning in action using SAS products. However, the curriculum is vendor-neutral and universally-applicable. The contents and learning objectives apply, regardless of which machine learning software tools you end up choosing to work with.
What You Will Learn
- Participate in the deployment of machine learning
- Identify potential machine learning deployments that will generate value for your organization
- Report on the predictive performance of machine learning and the profit it generates
- Understand the potential of machine learning and avoid the false promises of “artificial intelligence”
Course 1 of 3 in the Machine Learning for Everyone with Eric Siegel Specialization.
MODULE 0 - Introduction
What does this course – and the overall three-course specialization – cover and why is it right for you? Find out how this unique curriculum will empower you to generate value with machine learning. This module outlines the specialization's unusually holistic coverage and its applicability for both business-level and tech-focused learners. You'll see why this integrated coverage is a valuable place to begin, as you prepare to take on the end-to-end process of deploying machine learning. This module will orient you and frame the upcoming content – as such, it has no assessments.
The Impact of Machine Learning
This module covers the business value of machine learning, the very purpose that it serves. You'll see what kinds of business operations machine learning improves and how it improves them. And we'll lay the foundation: what the data needs to look like, what is learned from that data, and how the predictions generated by machine learning render all kinds of large-scale operations more effective.
Data: the New Oil
We are up to our ears in data, but how much can this raw material really tell us? And what actually makes it predictive? This module will show you what your data needs to look like before your computer can learn from it – the particular form and format – and you'll see the kinds of fascinating and bizarre predictive insights discovered within that data. Then we'll take the first steps in forming a predictive model, a mechanism that serves to combine such insights.
Predictive Models: What Gets Learned from Data
And now the main event: predictive modeling. This module will show you how software automatically generates a predictive model from data and the elegant trick that's universally applied in order to verify that the model actually works. We'll visually compare and contrast popular modeling methods and demonstrate how to draw a profit curve that estimates the bottom line that will be delivered by deploying a model. Then we'll take a hard look at both the potential and limits of machine learning – how far advanced methods like deep learning could propel us, and yet why fundamental data requirements ultimately impose certain restrictions.
Industry Perspective: AI Myths and Real Ethical Risks
Machine learning is sometimes referred to as "artificial intelligence", but that ill-defined term overpromises and confuses just as much as it elicits excitement. The first portion of this module will clear up common myths about AI and show you its downside, the costs incurred by legitimizing AI as a field. Then we'll turn to the great ethical responsibilities you are taking on by entering the field of machine learning. You'll see five ways that machine learning threatens social justice and we'll dive more deeply into one: discriminatory models that base their decisions in part on a protected class like race, religion, or sexual orientation. But then we'll shift gears and balance this out by defending machine learning, demonstrating all the good it does in the world and holding its criticisms up to a higher standard.