Machine learning with Pyspark : with natural language processing and recommender systems / Pramod Singh.
2022
QA76.76.A65 S55 2022
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Details
Title
Machine learning with Pyspark : with natural language processing and recommender systems / Pramod Singh.
Author
Singh, Pramod, author.
Edition
Second edition.
ISBN
9781484277775 (electronic bk.)
1484277775 (electronic bk.)
9781484277768
1484277767
1484277775 (electronic bk.)
9781484277768
1484277767
Published
California: Apress, [2022]
Language
English
Description
1 online resource
Other Standard Identifiers
10.1007/978-1-4842-7777-5 doi
Call Number
QA76.76.A65 S55 2022
Dewey Decimal Classification
005.7
Summary
Master the new features in PySpark 3.1 to develop data-driven, intelligent applications. This updated edition covers topics ranging from building scalable machine learning models, to natural language processing, to recommender systems. Machine Learning with PySpark, Second Edition begins with the fundamentals of Apache Spark, including the latest updates to the framework. Next, you will learn the full spectrum of traditional machine learning algorithm implementations, along with natural language processing and recommender systems. You'll gain familiarity with the critical process of selecting machine learning algorithms, data ingestion, and data processing to solve business problems. You'll see a demonstration of how to build supervised machine learning models such as linear regression, logistic regression, decision trees, and random forests. You'll also learn how to automate the steps using Spark pipelines, followed by unsupervised models such as K-means and hierarchical clustering. A section on Natural Language Processing (NLP) covers text processing, text mining, and embeddings for classification. This new edition also introduces Koalas in Spark and how to automate data workflow using Airflow and PySpark's latest ML library. After completing this book, you will understand how to use PySpark's machine learning library to build and train various machine learning models, along with related components such as data ingestion, processing and visualization to develop data-driven intelligent applications What you will learn: Build a spectrum of supervised and unsupervised machine learning algorithms Use PySpark's machine learning library to implement machine learning and recommender systems Leverage the new features in PySpark's machine learning library Understand data processing using Koalas in Spark Handle issues around feature engineering, class balance, bias and variance, and cross validation to build optimally fit models Who This Book Is For Data science and machine learning professionals.
Note
Includes index.
Access Note
Access limited to authorized users.
Source of Description
Description based on online resource; title from digital title page (viewed on February 15, 2022).
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Machine Learning with Pyspark
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Table of Contents
Chapter 1: Introduction to Spark 3.1
Chapter 2: Manage Data with PySpark
Chapter 3: Introduction to Machine Learning
Chapter 4: Linear Regression with PySpark
Chapter 5: Logistic Regression with PySpark
Chapter 6: Ensembling with PySpark
Chapter 7: Clustering with PySpark
Chapter 8: Recommendation Engine with PySpark
Chapter 9: Advanced Feature Engineering with PySpark.
Chapter 2: Manage Data with PySpark
Chapter 3: Introduction to Machine Learning
Chapter 4: Linear Regression with PySpark
Chapter 5: Logistic Regression with PySpark
Chapter 6: Ensembling with PySpark
Chapter 7: Clustering with PySpark
Chapter 8: Recommendation Engine with PySpark
Chapter 9: Advanced Feature Engineering with PySpark.