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The Practical Guide to Actionable Machine Learning

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Preparing data and putting predictive models to work usually requires a numerous set of different skills, tight collaborations with business and IT professionals, and long and intensive deployment efforts to operationalize machine learning within a business process.

Jen Underwood, from Impact Analytix, a recognized expert in the industry, takes a look at how to expedite the entire machine learning project life-cycle by evaluating new approaches and technologies. Her in depth guide, take a real-life example to provide practical and actionable recommendations to successful machine learning adoption.

This guide covers:

  • What modern machine learning life cycle is
  • Define machine learning objectives for success
  • How to assess, explore and prepare data for ML
  • How to automate and operationalize ML modeling