About this book series

Series Editor: Oge Marques

The Springer Series in Applied Machine Learning focuses on monographs, textbooks, edited volumes, and reference books that provide suitable content and educate the reader on how the theoretical approaches, algorithms, and techniques of machine learning can be applied to address real-world problems in a principled way.

The goal is a series of state-of-the-art books for the libraries of both machine learning scientists interested in applying the latest techniques to novel problems as well as practitioners, technologists, and researchers in various fields interested in leveraging the latest advances in machine learning for developing solutions that work for practical problems. The scope spans the breadth of machine learning and AI as it pertains to all application areas ─ both through books that address techniques specific to one application domain ─ and books that show the applicability of different types of machine learning methods to a wide array of domains.
Electronic ISSN
2520-1301
Print ISSN
2520-1298
Series Editor
  • Oge Marques

Book titles in this series

  1. Thinking Data Science

    A Data Science Practitioner’s Guide

    Authors:
    • Poornachandra Sarang
    • Copyright: 2023

    Available Renditions

    • Hard cover
    • Soft cover
    • eBook