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Testas Abdelaziz
Distributed Machine Learning With Pyspark Migrating Effortlessly From Pandas And Scikit - Learn
Distributed Machine Learning With Pyspark Migrating Effortlessly From Pandas And Scikit - Learn
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Binding: Paperback
Description: Migrate from pandas and scikit - learn to Py Spark to handle vast amounts of data and achieve faster data processing time. This book will show you how to make this transition by adapting your skills and leveraging the similarities in syntax functionality and interoperability between these tools. Distributed Machine Learning with Py Spark offers a roadmap to data scientists considering transitioning from small data libraries (pandas/scikit - learn) to big data processing and machine learning with Py Spark. You will learn to translate Python code from pandas/scikit - learn to Py Spark to preprocess large volumes of data and build train test and evaluate popular machine learning algorithms such as linear and logistic regression decision trees random forests support vector machines Na ve Bayes and neural networks. After completing this book you will understand the foundational concepts of data preparation and machine learning and will have the skills necessary toapply these methods using Py Spark the industry standard for building scalable ML data pipelines. What You Will Learn Master the fundamentals of supervised learning unsupervised learning NLP and recommender systems Understand the differences between Py Spark scikit - learn and pandas Perform linear regression logistic regression and decision tree regression with pandas scikit - learn and Py Spark Distinguish between the pipelines of Py Spark and scikit - learn Who This Book Is For Data scientists data engineers and machine learning practitioners who have some familiarity with Python but who are new to distributed machine learning and the Py Spark framework.
Title: Distributed Machine Learning With Pyspark Migrating Effortlessly From Pandas And Scikit - Learn
Author(s): Testas Abdelaziz
Publisher: Apress
Barcode: 9781484297506
Pages: 490 Pages, 8 Illustrations, Black And White; Xx, 490 P. 8 Illus.
Publication Date: 11/24/2023
Category: Machine Learning
Description: Migrate from pandas and scikit - learn to Py Spark to handle vast amounts of data and achieve faster data processing time. This book will show you how to make this transition by adapting your skills and leveraging the similarities in syntax functionality and interoperability between these tools. Distributed Machine Learning with Py Spark offers a roadmap to data scientists considering transitioning from small data libraries (pandas/scikit - learn) to big data processing and machine learning with Py Spark. You will learn to translate Python code from pandas/scikit - learn to Py Spark to preprocess large volumes of data and build train test and evaluate popular machine learning algorithms such as linear and logistic regression decision trees random forests support vector machines Na ve Bayes and neural networks. After completing this book you will understand the foundational concepts of data preparation and machine learning and will have the skills necessary toapply these methods using Py Spark the industry standard for building scalable ML data pipelines. What You Will Learn Master the fundamentals of supervised learning unsupervised learning NLP and recommender systems Understand the differences between Py Spark scikit - learn and pandas Perform linear regression logistic regression and decision tree regression with pandas scikit - learn and Py Spark Distinguish between the pipelines of Py Spark and scikit - learn Who This Book Is For Data scientists data engineers and machine learning practitioners who have some familiarity with Python but who are new to distributed machine learning and the Py Spark framework.
Title: Distributed Machine Learning With Pyspark Migrating Effortlessly From Pandas And Scikit - Learn
Author(s): Testas Abdelaziz
Publisher: Apress
Barcode: 9781484297506
Pages: 490 Pages, 8 Illustrations, Black And White; Xx, 490 P. 8 Illus.
Publication Date: 11/24/2023
Category: Machine Learning