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Contents

  • Introduction to Machine Learning (32 pg taken from my book)
  • Reasons why Machine Learning can fail (12 pg)
  • [Biz] Tutorial on improving your business by predicting churn (15 pg)
  • [Dev] Python notebooks on basics of ML APIs
  • Real-world datasets to experiment with
  • The unique ML Canvas and its guide to formalize your own use cases

Last update on January 8, 2017

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  • Summarized excerpts from my book: example use cases of ML, how to formalize your own use case, data preparation, tips to create value from data, etc.
  • Links to additional material: videos of presentations, hands-on demos, and some of my most popular articles.

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About the author

My name is Louis Dorard, I'm an independent consultant and my goal is to help people use ML to make their applications and their businesses smarter.

I'm the author of Bootstrapping Machine Learning, which is the first book to teach ML through the use of APIs, and I'm a co-founder of PAPIs.io, the world's first conference on Predictive Applications & APIs. 

I also teach Predictive Analytics at University College London School of Management.