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Machine Learning Background and Training Resources

Posted by Martin Davy on Jan 10, 2019 7:00:00 AM
Martin Davy
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Before I started at Manifold, I knew a little about the machine learning (ML) space, but wanted a better grounding in it. I asked CEO Vinay Seth Mohta for some more information, and found the resources he shared tremendously helpful. My research turned up some additional resources of my own, as well.

The following resource compilation includes those items, as well as a few added by others on the Manifold team. We hope to continue updating and improving this list, and may reshare it out periodically in the hopes that others 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

 

Topics: Machine learning

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