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Mastering Scala machine learning /
Advance your skills in efficient data analysis and data processing using the powerful tools of Scala, Spark, and HadoopAbout This Book*This is a primer on functional-programming-style techniques to help you efficiently process and analyze all of your data*Get acquainted with the best and newest tool...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
2016.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 520 | 8 | |a Advance your skills in efficient data analysis and data processing using the powerful tools of Scala, Spark, and HadoopAbout This Book*This is a primer on functional-programming-style techniques to help you efficiently process and analyze all of your data*Get acquainted with the best and newest tools available such as Scala, Spark, Parquet and MLlib for machine learning*Learn the best practices to incorporate new Big Data machine learning in your data-driven enterprise to gain future scalability and maintainabilityWho This Book Is ForMastering Scala Machine Learning is intended for enthusiasts who want to plunge into the new pool of emerging techniques for machine learning. Some familiarity with standard statistical techniques is required. What You Will Learn*Sharpen your functional programming skills in Scala using REPL*Apply standard and advanced machine learning techniques using Scala*Get acquainted with Big Data technologies and grasp why we need a functional approach to Big Data*Discover new data structures, algorithms, approaches, and habits that will allow you to work effectively with large amounts of data*Understand the principles of supervised and unsupervised learning in machine learning*Work with unstructured data and serialize it using Kryo, Protobuf, Avro, and AvroParquet*Construct reliable and robust data pipelines and manage data in a data-driven enterprise*Implement scalable model monitoring and alerts with ScalaIn DetailSince the advent of object-oriented programming, new technologies related to Big Data are constantly popping up on the market. One such technology is Scala, which is considered to be a successor to Java in the area of Big Data by many, like Java was to C/C++ in the area of distributed programing. This book aims to take your knowledge to next level and help you impart that knowledge to build advanced applications such as social media mining, intelligent news portals, and more. After a quick refresher on functional programming concepts using REPL, you will see some practical examples of setting up the development environment and tinkering with data. We will then explore working with Spark and MLlib using k-means and decision trees. Most of the data that we produce today is unstructured and raw, and you will learn to tackle this type of data with advanced topics such as regression, classification, integration, and working with graph algorithms. Finally, you will discover at how to use Scala to perform complex concept analysis, to monitor model performance, and to build a model repository. By the end of this book, you will have gained expertise in performing Scala machine learning and will be able to build complex machine learning projects using Scala. | |
| 505 | 0 | |a Cover; Copyright; Credits; About the Author; Acknowlegement; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Exploratory Data Analysis; Getting started with Scala; Distinct values of a categorical field; Summarization of a numeric field; Grepping across multiple fields; Basic, stratified, and consistent sampling; Working with Scala and Spark Notebooks; Basic correlations; Summary; Chapter 2: Data Pipelines and Modeling; Influence diagrams; Sequential trials and dealing with risk; Exploration and exploitation; Unknown unknowns; Basic components of a data-driven system; Data ingest. | |
| 505 | 8 | |a Data transformation layerData analytics and machine learning; UI component; Actions engine; Correlation engine; Monitoring; Optimization and interactivity; Feedback loops; Summary; Chapter 3: Working with Spark and MLlib; Setting up Spark; Understanding Spark architecture; Task scheduling; Spark components; MQTT, ZeroMQ, Flume, and Kafka; HDFS, Cassandra, S3, and Tachyon; Mesos, YARN, and Standalone; Applications; Word count; Streaming word count; Spark SQL and DataFrame; ML libraries; SparkR; Graph algorithms -- GraphX and GraphFrames; Spark performance tuning; Running Hadoop HDFS; Summary. | |
| 505 | 8 | |a Chapter 4: Supervised and Unsupervised LearningRecords and supervised learning; Iris dataset; Labeled point; SVMWithSGD; Logistic regression; Decision tree; Bagging and boosting -- ensemble learning methods; Unsupervised learning; Problem dimensionality; Summary; Chapter 5: Regression and Classification; What regression stands for?; Continuous space and metrics; Linear regression; Logistic regression; Regularization; Multivariate regression; Heteroscedasticity; Regression trees; Classification metrics; Multiclass problems; Perceptron; Generalization error and overfitting; Summary. | |
| 505 | 8 | |a Chapter 6: Working with Unstructured DataNested data; Other serialization formats; Hive and Impala; Sessionization; Working with traits; Working with pattern matching; Other uses of unstructured data; Probabilistic structures; Projections; Summary; Chapter 7: Working with Graph Algorithms; A quick introduction to graphs; SBT; Graph for Scala; Adding nodes and edges; Graph constraints; JSON; GraphX; Who is getting e-mails?; Connected components; Triangle counting; Strongly connected components; PageRank; SVD++; Summary; Chapter 8: Integrating Scala with R and Python; Integrating with R. | |
| 505 | 8 | |a Setting up R and SparkRLinux; Mac OS; Windows; Running SparkR via scripts; Running Spark via R's command line; DataFrames; Linear models; Generalized linear model; Reading JSON files in SparkR; Writing Parquet files in SparkR; Invoking Scala from R; Using Rserve; Integrating with Python; Setting up Python; PySpark; Calling Python from Java/Scala; Using sys.process._; Spark pipe; Jython and JSR 223; Summary; Chapter 9: NLP in Scala; Text analysis pipeline; Simple text analysis; MLlib algorithms in Spark; TF-IDF; LDA; Segmentation, annotation, and chunking; POS tagging. | |
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