Introduction to Machine Learning

Washington D.C. campuses

GA Washington D.C. (1776 8th Floor)
1133 15th Street NW, 8th Floor
Washington DC 20005

Past Locations for this Class

Introduction to Machine Learning

Washington D.C.

Washington D.C. campuses

GA Washington D.C. (1776 8th Floor)
1133 15th Street NW, 8th Floor
Washington DC 20005

Past Locations for this Class

About this class

Machine learning is the art of teaching computers to learn and act on specific things without having to be programmed. Andrew Ng at Stanford says "In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome."

Ever wanted to go beyond your linear model in Microsoft Excel? This class is a high-level introduction to machine learning models and two types of algorithms: supervised (regression) and unsupervised (classification) models.

  • Both models are used to answer a wide variety of data problems you might encounter in a web startup:
  • Can I cluster my users into customer archetypes to better understand them?
  • What’s the probability that a new user with certain demographics and behavior will spend > $10 on my game?
  • How can I recommend similar products or features that my users might like?

By the time you finish this class, you'll have an idea of different types of algorithms and models, and situations in which you might want to use them. You’ll also have some resources and advice on how to specialize in machine learning, or become literate enough to hire a machine learning specialist in your organization.

Takeaways

  • An introduction to machine learning models, regression vs. classification, and use cases for both in predictive analytics.
  • A high-level look at cross-validation to gauge accuracy of your models.
  • Tools and resources to help you find answers to questions that may come up in future models and scenarios.

Prereqs & Preparation

Beginner/Intermediate. This is a general survey of machine learning algorithms and is not meant as a theoretical introduction, or technical construction of these algorithms. This class will be more applied and comprehensive than it is rigorous and theoretical. Resources on where you can learn more of the theory will be provided.

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