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Full Description
Access real-world documentation and examples for the Spark platform for building large-scale, enterprise-grade machine learning applications.
The past decade has seen an astonishing series of advances in machine learning. These breakthroughs are disrupting our everyday life and making an impact across every industry.
Next-Generation Machine Learning with Spark provides a gentle introduction to Spark and Spark MLlib and advances to more powerful, third-party machine learning algorithms and libraries beyond what is available in the standard Spark MLlib library. By the end of this book, you will be able to apply your knowledge to real-world use cases through dozens of practical examples and insightful explanations.
What You Will Learn
Be introduced to machine learning, Spark, and Spark MLlib 2.4.x
Achieve lightning-fast gradient boosting on Spark with the XGBoost4J-Spark and LightGBM libraries
Detect anomalies with the Isolation Forest algorithm for Spark
Use the Spark NLP and Stanford CoreNLP libraries that support multiple languages
Optimize your ML workload with the Alluxio in-memory data accelerator for Spark
Use GraphX and GraphFrames for Graph Analysis
Perform image recognition using convolutional neural networks
Utilize the Keras framework and distributed deep learning libraries with Spark
Who This Book Is For
Data scientists and machine learning engineers who want to take their knowledge to the next level and use Spark and more powerful, next-generation algorithms and libraries beyond what is available in the standard Spark MLlib library; also serves as a primer for aspiring data scientists and engineers who need an introduction to machine learning, Spark, and Spark MLlib.
Contents
Chapter 1: Introduction to Machine Learning.- Chapter 2: Introduction to Spark and Spark Mllib.- Chapter 3: Supervised Learning.- Chapter 4: Unsupervised Learning.- Chapter 5: Recommendations.- Chapter 6: Graph Analysis.- Chapter 7: Deep Learning.-
