How to Get Started with Machine Learning—for Executives and Engineers

The machine learning (ML) space can seem intimidating: Where do you start, and how does it all connect? At Manifold, we've compiled a list of resources borne of our expertise that we think will help you form a strong ML foundation. We hope to continue updating and improving this list, and may reshare it out periodically in the hopes that those who embark on this journey can have an even smoother and more fulfilling experience.

Some of these topics may already be familiar to you and some less so; feel free to skip through things you already know. And let us know in the Comments below what else you would add to the list. Enjoy!

There are three sections to this content

  1. Business-oriented content for executives about ML (generally quick reads)
  2. Technical content about ML (think survey papers)
  3. Hands-on-keyboard courses/projects (tinkering for an hour to a few days per project)

1. Business-oriented content for executives about ML

  • McKinsey piece on Exec's guide to AI
    • This piece does a nice job of explaining why we are seeing an explosion in machine learning now, the major categories of machine learning including deep learning, and the classes of business problems that are amenable to each approach.
  • Dataiku whitepaper on different types of ML
    • This piece, while a bit heavy on the marketing (it's by a product company) does explain in more technical detail some of the machine learning approaches and the pros/cons of each.
  • Ways to think about machine learning — Ben Evans
    • Framework for thinking about ML problems: Known data, known problems; known data, new questions; new data, new questions.
  • AI for the Real World — Tom Davenport & Rajeev Ronanki
    • While the article is heavy on RPA (a Deloitte area of focus), it makes the nice distinction between moonshot projects and incremental wins like IT staffing, with MD Anderson as a case study.
  • AI Transformation Playbook
    • Andrew Ng (of quite a bit of fame) is now part of an AI consulting company also. He wrote up a nice piece about AI Transformation at large organizations.

Somewhat more technical, but not totally technical

2. Technical Content about ML

3. Hands-on-Keyboard courses/projects

Martin Davy, Manifold Alum
Martin Davy was VP of Engineering at Manifold. Prior to Manifold, he was Executive Vice President & Chief Platform Architect at Houghton Mifflin Harcourt, where he was responsible for all aspects of their global Software Engineering division. He has also served as Global Head of Trading Engineering for Fidelity Investments.