The Power of Machine Learning: Boost Business, Accumulate Clicks, Fight Fraud, and Deny Deadbeats

SAS via Coursera

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Overview

It's the age of machine learning. Companies are seizing upon the power of this technology to combat risk, boost sales, cut costs, block fraud, streamline manufacturing, conquer spam, toughen crime fighting, and win elections. Want to tap that potential? It's best to start with a holistic, business-oriented course on machine learning no matter whether youre more on the tech or the business side. After all, successfully deploying machine learning relies on savvy business leadership just as much as it...

Syllabus

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.

MODULE 1 - 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.

MODULE 2 - 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.

MODULE 3 - 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.

MODULE 4 - 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.