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

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. Spis treści: 1. Spark for Machine Learning2. Data Preparation for ML on Spark 3. Holistic View on Spark4. Rapid Fraud Detection on Spark 5. Risk Scoring on Spark6. Scalable Churn Prediction on Spark7. Parallel Computing for Recommendation on Spark 8. Learning Analytics on Spark9. City Analytics on Spark10. Learning Telco Data on Spark11. Modeling Open Data on Spark O autorze: Alex Liu is an expert in research methods and data science. He is currently one of IBM's leading experts in Big Data analytics and also a lead data scientist, where he serves big corporations, develops Big Data analytics IPs, and speaks at industrial conferences such as STRATA, Insights, SMAC, and BigDataCamp. In the past, Alex served as chief or lead data scientist for a few companies, including Yapstone, RS, and TRG. Before this, he was a lead consultant and director at RMA, where he provided data analytics consultation and training to many well-known organizations, including the United Nations, Indymac, AOL, Ingram Micro, GEM, Farmers Insurance, Scripps Networks, Sears, and USAID. At the same time, he taught advanced research methods to PhD candidates at University of Southern California and University of California at Irvine. Before this, he worked as a managing director for CATE/GEC and as a research fellow for the Asia/Pacific Research Center at Stanford University. Alex has a Ph.D. in quantitative sociology and a master's degree of science in statistical computing from Stanford University.