This course will show you how machine learning works. It covers the foundational underpinnings, the way insights are gleaned from data, how we can trust these insights are reliable, and how well predictive models perform – which can be established with pretty straightforward arithmetic. These are things every business professional needs to know, in addition to the quants.
And this course continues beyond machine learning standards to also cover cutting-edge, advanced methods, as well as preparing you to circumvent prevalent pitfalls that seldom receive the attention they deserve. The course dives deeply into these topics, and yet remains accessible to non-technical learners and newcomers.
Learn How To
How predictive modeling algorithms work, including decision trees, logistic regression, and neural networksTreacherous pitfalls such as overfitting, p-hacking, and presuming causation from correlationsHow to interpret a predictive model in detail and explain how it worksAdvanced methods such as ensembles and uplift modeling (aka persuasion modeling)How to pick a tool, selecting from the many machine learning software optionsHow to evaluate a predictive model, reporting on its performance in business termsHow to screen a predictive model for potential bias against protected classes – aka AI ethicsPrerequisites
Before this course, learners should take the first two of this specialization's three courses, The Power of Machine Learning and Launching Machine Learning.
