Details zum E-Book

Apache Spark 2.x Machine Learning Cookbook. Over 100 recipes to simplify machine learning model implementations with Spark

Apache Spark 2.x Machine Learning Cookbook. Over 100 recipes to simplify machine learning model implementations with Spark

Siamak Amirghodsi, Shuen Mei, Meenakshi Rajendran, Broderick Hall

E-book
Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability, and optimization. Learning about algorithms enables a wide range of applications, from everyday tasks such as product recommendations and spam filtering to cutting edge applications such as self-driving cars and personalized medicine. You will gain hands-on experience of applying these principles using Apache Spark, a resilient cluster computing system well suited for large-scale machine learning tasks.
This book begins with a quick overview of setting up the necessary IDEs to facilitate the execution of code examples that will be covered in various chapters. It also highlights some key issues developers face while working with machine learning algorithms on the Spark platform. We progress by uncovering the various Spark APIs and the implementation of ML algorithms with developing classification systems, recommendation engines, text analytics, clustering, and learning systems. Toward the final chapters, we’ll focus on building high-end applications and explain various unsupervised methodologies and challenges to tackle when implementing with big data ML systems.
  • 1. Practical Machine Learning with Spark using Scala
  • 2. Just enough Linear Algebra for Machine Learning with Spark
  • 3. Spark’s three data musketeers for machine learning – Perfect Together
  • 4. Common Recipes for Implementing a Robust Machine Learning System
  • 5. Practical Machine Learning with Regression and Classification in Spark 2.0 – Part I
  • 6. Practical Machine Learning with Regression and Classification in Spark 2.0 – Part II
  • 7. Recommendation engine that scales with Spark
  • 8. Unsupervised Clustering with Apache Spark 2.0
  • 9. Optimization – Going Down the Hill with the Gradient Descent
  • 10. Build Machine Learning Systems with Decision Tree and Ensemble Models
  • 11. Curse of high-dimensionality in Big Data
  • 12. Implementing Text Analytics with Spark 2.0 ML Library
  • 13. Spark Streaming and Machine Learning Library
  • Titel: Apache Spark 2.x Machine Learning Cookbook. Over 100 recipes to simplify machine learning model implementations with Spark
  • Autor: Siamak Amirghodsi, Shuen Mei, Meenakshi Rajendran, Broderick Hall
  • Originaler Titel: Apache Spark 2.x Machine Learning Cookbook. Over 100 recipes to simplify machine learning model implementations with Spark
  • ISBN: 9781782174608, 9781782174608
  • Veröffentlichungsdatum: 2017-09-22
  • Format: E-book
  • Artikelkennung: e_15pp
  • Verleger: Packt Publishing