Cracking the Code: Machine Learning Made Understandable - Christoph Molnar

DataTalks.Club

We talked about:

  • Christoph’s background
  • Kaggle and other competitions
  • How Christoph became interested in interpretable machine learning
  • Interpretability vs Accuracy
  • Christoph’s current competition engagement
  • How Christoph chooses topics for books
  • Why Christoph started the writing journey with a book
  • Self-publishing vs via a publisher
  • Christoph’s other books
  • What is conformal prediction?
  • Christoph’s book on SHAP
  • Explainable AI vs Interpretable AI
  • Working alone vs with other people
  • Christoph’s other engagements and how to stay hands-on
  • Keeping a logbook
  • Does one have to be an expert on the topic to write a book about it?
  • Writing in the open and other feedback gathering methods
  • Advice for those who want to be technical writers
  • Self-publishing tools
  • Finding Christoph online

Links:

  • LinkedIn: https://www.linkedin.com/in/christoph-molnar/
  • Website: https://christophmolnar.com/

Free ML Engineering course: http://mlzoomcamp.com Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

To listen to explicit episodes, sign in.

Stay up to date with this show

Sign in or sign up to follow shows, save episodes, and get the latest updates.

Select a country or region

Africa, Middle East, and India

Asia Pacific

Europe

Latin America and the Caribbean

The United States and Canada