Szczegóły ebooka

Apache Spark Machine Learning Blueprints. Develop a range of cutting-edge machine learning projects with Apache Spark using this actionable guide

Apache Spark Machine Learning Blueprints. Develop a range of cutting-edge machine learning projects with Apache Spark using this actionable guide

Alex Liu

Ebook
There's a reason why Apache Spark has become one of the most popular tools in Machine Learning – its ability to handle huge datasets at an impressive speed means you can be much more responsive to the data at your disposal. This book shows you Spark at its very best, demonstrating how to connect it with R and unlock maximum value not only from the tool but also from your data.
Packed with a range of project blueprints that demonstrate some of the most interesting challenges that Spark can help you tackle, you'll find out how to use Spark notebooks and access, clean, and join different datasets before putting your knowledge into practice with some real-world projects, in which you will see how Spark Machine Learning can help you with everything from fraud detection to analyzing customer attrition. You'll also find out how to build a recommendation engine using Spark's parallel computing powers.
  • 1. Spark for Machine Learning
  • 2. Data Preparation for ML on Spark
  • 3. Holistic View on Spark
  • 4. Rapid Fraud Detection on Spark
  • 5. Risk Scoring on Spark
  • 6. Scalable Churn Prediction on Spark
  • 7. Parallel Computing for Recommendation on Spark
  • 8. Learning Analytics on Spark
  • 9. City Analytics on Spark
  • 10. Learning Telco Data on Spark
  • 11. Modeling Open Data on Spark
  • Tytuł: Apache Spark Machine Learning Blueprints. Develop a range of cutting-edge machine learning projects with Apache Spark using this actionable guide
  • Autor: Alex Liu
  • Tytuł oryginału: Apache Spark Machine Learning Blueprints. Develop a range of cutting-edge machine learning projects with Apache Spark using this actionable guide
  • ISBN: 9781785887789, 9781785887789
  • Data wydania: 2016-05-30
  • Format: Ebook
  • Identyfikator pozycji: e_3cmu
  • Wydawca: Packt Publishing