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Machine Learning with PySpark: With Natural Language Processing and Recommender Systems
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
Build a spectrum of supervised and unsupervised machine learning algorithmsUse PySpark's machine learning library to implement machine learning and recommender systems Leverage the new features in PySpark’s machine learning libraryUnderstand data processing using Koalas in Spark Handle issues around feature engineering, class balance, bias andvariance, and cross validation to build optimally fit models
Who This Book Is For
Data science and machine learning professionals.
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
Build a spectrum of supervised and unsupervised machine learning algorithmsUse PySpark's machine learning library to implement machine learning and recommender systems Leverage the new features in PySpark’s machine learning libraryUnderstand data processing using Koalas in Spark Handle issues around feature engineering, class balance, bias andvariance, and cross validation to build optimally fit models
Who This Book Is For
Data science and machine learning professionals.
297 pages, Kindle Edition
Published December 8, 2021
12 people want to read
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Displaying 1 of 1 review
January 28, 2020
Depending on your goals, this book could be good, or a waste of time:
If your goal is to have a really quick and super simple recap of what each of the main algorithms in ML does, and how to equally quickly and simply implement them with PySpark (on really simple datasets, many of them made up), then this is your book. It is very easy to read, and the author goes right to the point, in the explanations and the implementations.
If you are not in a hurry and your goal is to learn a lot about PySpark and its details, or to learn how to implement a comprehensive and nuanced ML pipeline, do some detailed EDA, build models, evaluate them and improve them, all this on some real datasets to answer interesting questions... then this is NOT your book. In this case I'd recommend "Learning Pyspark", by Tomasz Drabas and Denny Lee. Far more comprehensive, well-explained and complex.
Having said that, this is a good book to have if you just want to do a mini-PoC of each main ML algorithm with a notebook in a couple of afternoons.
If your goal is to have a really quick and super simple recap of what each of the main algorithms in ML does, and how to equally quickly and simply implement them with PySpark (on really simple datasets, many of them made up), then this is your book. It is very easy to read, and the author goes right to the point, in the explanations and the implementations.
If you are not in a hurry and your goal is to learn a lot about PySpark and its details, or to learn how to implement a comprehensive and nuanced ML pipeline, do some detailed EDA, build models, evaluate them and improve them, all this on some real datasets to answer interesting questions... then this is NOT your book. In this case I'd recommend "Learning Pyspark", by Tomasz Drabas and Denny Lee. Far more comprehensive, well-explained and complex.
Having said that, this is a good book to have if you just want to do a mini-PoC of each main ML algorithm with a notebook in a couple of afternoons.
Displaying 1 of 1 review