The Power of Machine Learning


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.

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 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’ll 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”

What you will learn from this course

Module 1 – Introduction

  ➤ Machine learning specialization overview

Why this course and what you will learn

 Vendor-neutral courses

 Exploring SAS, Visual Data Mining, and Machine Learning

Module 2 – The impact of Machine Learning

                            Forecasting vs. predictive analytics

                           The full definitions of machine learning and predictive


                            The two stages of machine learning: modeling and


                           Targeting marketing with response modeling

                           ➤  The Prediction effect

➤  Targeted customer retention with churn modelling

 Financial credit risk

➤  “Non-predictive” applications: detection and  Classification

➤  Why ML is the latest evolutionary step of the Information Age

Module 3 – Data

                        Introduction to Big Data

                        A paradigm shift for scientific

                        discovery: its automation

                        The Data Effect: Data is always predictive

                        Training data — what it looks like

                        Predicting with one single variable

Growing a decision tree to combine  variables

More on decision trees

The light bulb puzzle

Measuring predictive performance

 ➤ DEMO – Training a simple decision  tree model (optional)

Module 4 – Predictive Models

                          ➤ The principles of predictive modelling

                          ➤ How can you trust a predictive model

                          ➤ More predictive modeling principles

                          ➤ Visually comparing modeling methods

                               – decision boundaries

                          ➤ DEMO – Training and comparing multiple models

                          ➤ Deploying a predictive model

The profit curve of a model

Deployment results in targeting marketing and sales

Deep learning – application areas and limitations

Labeled data: a source of great power, yet a major limitation

Talking computers — natural language processing and text analytics

Module 5 – Industry Perspective: AI Myths and Real Ethical Risks

                         ➤ Dismantling the logical fallacy that is AI

                         ➤ Why legitimizing AI as a field incurs great cost

                         ➤ Ethics overview: five ways ML threatens social justice

                         ➤ Blatantly discriminatory models

                         ➤ The trend towards discriminatory models

The argument against discriminatory models

Five myths about “evil” big data

Defending machine learning — how it does good

Course wrap-up