Machine learning with Pyspark : with natural language processing and recommender systems. / Pramod Singh.

Format:

Book

Author/Creator:

Singh, Pramod, 1954- author.

Language:

English

Subjects (All):

Application software--Development.

Application software.

Python (Computer program language).

SPARK (Computer program language).

Physical Description:

1 online resource (230 pages)

Edition:

Second edition.

Place of Publication:

New York, NY : Apress, [2022]

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.

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.

Notes:

Description based on print version record.

Includes index.

Other Format:

Print version: Singh, Pramod Machine Learning with Pyspark

ISBN:

9781523150991

1523150998

9781484277775

1484277775

OCLC:

1290020510