Machine Learning with Spark and Python

Michael Bowles

ISBN 10: 1119561930 / ISBN 13: 9781119561934

Edité par John Wiley and Sons Inc, US, 2019

A propos de cet article

Machine Learning with Spark and Python Essential Techniques for Predictive Analytics, Second Edition simplifies ML for practical uses by focusing on two key algorithms. This new second edition improves with the addition of Spark-a ML framework from the Apache foundation. By implementing Spark, machine learning students can easily process much large data sets and call the spark algorithms using ordinary Python code.   Machine Learning with Spark and Python focuses on two algorithm families (linear methods and ensemble methods) that effectively predict outcomes. This type of problem covers many use cases such as what ad to place on a web page, predicting prices in securities markets, or detecting credit card fraud. The focus on two families gives enough room for full descriptions of the mechanisms at work in the algorithms. Then the code examples serve to illustrate the workings of the machinery with specific hackable code.

N° de réf. du vendeur LU-9781119561934

Détails bibliographiques

• Titre: Machine Learning with Spark and Python
• Éditeur: John Wiley and Sons Inc, US
• Date de parution: 2019
• Langue: anglais
• ISBN à 10 chiffres: 1119561930
• ISBN à 13 chiffres: 9781119561934
• Reliure: Paperback
• Édition: 2ème Édition
• État de l'article: New

A propos de ce titre

Synopsis

Machine Learning with Spark and Python Essential Techniques for Predictive Analytics, Second Edition simplifies ML for practical uses by focusing on two key algorithms. This new second edition improves with the addition of Spark--a ML framework from the Apache foundation. By implementing Spark, machine learning students can easily process much large data sets and call the spark algorithms using ordinary Python code.

Machine Learning with Spark and Python focuses on two algorithm families (linear methods and ensemble methods) that effectively predict outcomes. This type of problem covers many use cases such as what ad to place on a web page, predicting prices in securities markets, or detecting credit card fraud. The focus on two families gives enough room for full descriptions of the mechanisms at work in the algorithms. Then the code examples serve to illustrate the workings of the machinery with specific hackable code.

À propos de l?auteur

MICHAEL BOWLES teaches machine learning at UC Berkeley, University of New Haven and Hacker Dojo in Silicon Valley, consults on machine learning projects, and is involved in a number of startups in such areas as semi conductor inspection, drug design and optimization and trading in the financial markets. Following an assistant professorship at MIT, Michael went on to found and run two Silicon Valley startups, both of which went public. His courses are always popular and receive great feedback from participants.