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Interpretable Machine Learning with Python

Learn to build interpretable high-performance models with hands-on real-world examples

Serg Masís

Interpretable Machine Learning with Python
Interpretable Machine Learning with Python

Interpretable Machine Learning with Python

Learn to build interpretable high-performance models with hands-on real-world examples

Serg Masís

Paperback | English
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Description

Serg Masís has been at the confluence of the internet, application development, and analytics for the last two decades. Currently, he's a climate and agronomic data scientist at Syngenta, a leading agribusiness company with a mission to improve global food security. Before that role, he co-founded a start-up, incubated by Harvard Innovation Labs, that combined the power of cloud computing and machine learning with principles in decision-making science to expose users to new places and events. Whether it pertains to leisure activities, plant diseases, or customer lifetime value, Serg is passionate about providing the often-missing link between data and decision-making—and machine learning interpretation helps bridge this gap robustly.

Serg Masís has been at the confluence of the internet, application development, and analytics for the last two decades. Currently, he's a climate and agronomic data scientist at Syngenta, a leading agribusiness company with a mission to improve global food security. Before that role, he co-founded a start-up, incubated by Harvard Innovation Labs, that combined the power of cloud computing and machine learning with principles in decision-making science to expose users to new places and events. Whether it pertains to leisure activities, plant diseases, or customer lifetime value, Serg is passionate about providing the often-missing link between data and decision-making—and machine learning interpretation helps bridge this gap robustly.

Specifications

  • Publisher
    Packt Publishing Limited
  • Pub date
    Mar 2021
  • Pages
    736
  • Theme
    Machine learning
  • Dimensions
    235 x 191 mm
  • EAN
    9781800203907
  • Paperback
    Paperback
  • Language
    English

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