Machine Learning with PySpark
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491_9781484277768
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Chapter 1: Introduction to Spark 3.1Chapter Goal: The books opening chapter introduces the readers to latest changes in PySpark and updates to the framework. This chapter covers the different components of Spark ecosystem. The chapter doubles up as an introduction to the books format, including explanation of formatting practices, pointers to the books accompanying codebase online, and support contact information. The chapter sets readers expectations in terms of the content and structure of the rest of the book. This chapter provides the audience with a set of required libraries and code/data download information so that the user is able to set up their environment appropriately.No of pages-30Sub -Topics1.Data status2.Apache Spark evolution3.Apache Spark fundamentals4.Spark components5.Setting up Spark 3.1Chapter 2: Manage Data with PySparkChapter Goal: This chapter covers the steps right from reading the data, pre-processing and cleaning for machine learning purpose. The chapter showcases the steps to build end to end data handling pipelines to transform and create features for machine learning. It covers simple way to use Koalas in order to leverage pandas in a distributed way in Spark.It also covers the method to automate the data scripts in order to run schedules data jobs using Airflow.No of pages:50Sub - Topics1. Data ingestion2. Data cleaning3. Data transformation4.End- to end data pipelines5.Data processing using koalas in Spark on Pandas DataFrame6.Automate data workflow using Airflow Chapter 3: Introduction to Machine LearningChapter Goal:This chapter introduces the readers to basic fundamentals of machine learning. This chapter covers different categories of machine learning and different stages in the machine learning lifecycle. It highlights the method to extract information related to model interpretation to understand the reasoning behind model predictions in PySpark . No of pages: 25Sub - Topics: 1.Supervise
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