Build machine learning models, natural language processing applications, and recommender systems with PySpark to solve various business challenges. This book starts with the fundamentals of Spark and its evolution and then covers the entire spectrum of traditional machine learning algorithms along with natural language processing and recommender systems using PySpark.
Machine Learning with PySpark shows you how to build supervised machine learning models such as linear regression, logistic regression, decision trees, and random forest. You’ll also see unsupervised machine learning models such as K-means and hierarchical clustering. A major portion of the book focuses on feature engineering to create useful features with PySpark to train the machine learning models. The natural language processing section covers text processing, text mining, and embedding for classification.
After reading this book, you will understand how to use PySpark’s machine learning library to build and train various machine learning models. Additionally you’ll become comfortable with related PySpark components, such as data ingestion, data processing, and data analysis, that you can use to develop data-driven intelligent applications.

What You Will Learn
• Build a spectrum of supervised and unsupervised machine learning algorithms
• Implement machine learning algorithms with Spark MLlib libraries
• Develop a recommender system with Spark MLlib libraries
• Handle issues related to feature engineering, class balance, bias and variance, and cross validation for building an optimal fit model

Who This Book Is For

Data science and machine learning professionals.

用PySpark建立机器学习模型、自然语言处理应用和推荐系统，以解决各种商业挑战。本书从Spark的基本原理及其演变开始，然后涵盖了传统机器学习算法的全部内容，以及使用PySpark的自然语言处理和推荐系统。

PySpark的机器学习》向你展示了如何建立有监督的机器学习模型，如线性回归、逻辑回归、决策树和随机森林。你还会看到无监督的机器学习模型，如K-means和分层聚类。本书的一个主要部分集中在特征工程上，用PySpark创建有用的特征来训练机器学习模型。自然语言处理部分涵盖了文本处理、文本挖掘和分类的嵌入。

读完本书后，你将了解如何使用PySpark的机器学习库来建立和训练各种机器学习模型。此外，你还会对相关的PySpark组件（如数据摄取、数据处理和数据分析）感到满意，你可以用它来开发数据驱动的智能应用程序。

你将会学到什么

- 建立一系列有监督和无监督的机器学习算法

- 用Spark MLlib库实现机器学习算法

- 使用Spark MLlib库开发一个推荐系统

- 处理与特征工程、类平衡、偏差和方差以及交叉验证有关的问题，以建立一个最佳拟合模型

本书适用对象

数据科学和机器学习专业人士。

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