Kim Falk

KIM FALK is a data scientist who is experienced building data-driven applications. He’s passionate about recommender systems and machine learning in general. He has trained recommender systems to provide movie choices to end users as well as ads to people, and has even helped attorneys find case law content. He’s worked with Big Data solutions and machine learning since 2010. Kim often speaks and writes about recommender systems. You can find him at http://kimfalk.org.

When he isn’t teaching machines to stalk people, Kim is a family man, father, and trail runner with his German Pointer.

books & projects by Kim Falk

Build an ML Recommender System
4 weeks · 4-5 hours per week · INTERMEDIATE
Included with a Manning Online subscription
catalog / Data Science

Recommender systems are one of the most popular and lucrative uses of machine learning, allowing businesses and organizations to give personalized suggestions to their customers.

In this liveProject, you’ll use common tools of the Python data ecosystem to design, build, and evaluate a movie recommendation model for the movie website. You’ll kick off your project by building simple genre charts, then work with existing movie rating data to implement personalized recommendations for each customer. When you’ve completed this hands-on and interesting project, you’ll have mastered a cornerstone technique of machine learning that’s in demand across companies and industries.

Practical Recommender Systems
  • January 2019
  • ISBN 9781617292705
  • 432 pages
Included with a Manning Online subscription
  • printed in black & white
  • Available translations: Korean, Polish, Russian, Simplified Chinese
catalog / Data Science / Data Engineering / Recommender Systems

Practical Recommender Systems explains how recommender systems work and shows how to create and apply them for your site. After covering the basics, you’ll see how to collect user data and produce personalized recommendations. You’ll learn how to use the most popular recommendation algorithms and see examples of them in action on sites like Amazon and Netflix. Finally, the book covers scaling problems and other issues you’ll encounter as your site grows.