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Distributed machine learning with Py...

Testas, Abdelaziz.

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Distributed machine learning with Pyspark = migrating effortlessly from Pandas and Scikit-Learn /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Distributed machine learning with Pyspark/ by Abdelaziz Testas.
Reminder of title:	migrating effortlessly from Pandas and Scikit-Learn /
Author:	Testas, Abdelaziz.
Published:	Berkeley, CA :Apress : : 2023.,
Description:	xx, 490 p. :illustrations, digital ;24 cm.
[NT 15003449]:	Chapter 1: An Easy Transition -- Chapter 2: Selecting Algorithms -- Chapter 3: Multiple Linear Regression with Pandas, Scikit-Learn, and PySpark -- Chapter 4: Decision Trees for Regression with Pandas, Scikit-Learn, and PySpark -- Chapter 5: Random Forests for Regression with Pandas, Scikit-Learn, and PySpark -- Chapter 6: Gradient-Boosted Tree Regression with Pandas, Scikit-Learn and PySpark -- Chapter 7: Logistic Regression with Pandas, Scikit-Learn and PySpark --  Chapter 8: Decision Tree Classification with Pandas, Scikit-Learn and PySpark -- Chapter 9: Random Forest Classification with Scikit-Learn and PySpark -- Chapter 10: Support Vector Machine Classification with Pandas, Scikit-Learn and PySpark -- Chapter 11: Naïve Bayes Classification with Pandas, Scikit-Learn and PySpark -- Chapter 12: Neural Network Classification with Pandas, Scikit-Learn and PySpark -- Chapter 13: Recommender Systems with Pandas, Surprise and PySpark -- Chapter 14: Natural Language Processing with Pandas, Scikit-Learn and PySpark -- Chapter 15: K-Means Clustering with Pandas, Scikit-Learn and PySpark -- Chapter 16: Hyperparameter Tuning with Scikit-Learn and PySpark -- Chapter 17: Pipelines with Scikit-Learn and PySpark -- Chapter 18: Deploying Models in Production with Scikit-Learn and PySpark.  .
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-1-4842-9751-3
ISBN:	9781484297513

Distributed machine learning with Pyspark = migrating effortlessly from Pandas and Scikit-Learn /
Testas, Abdelaziz.

Distributed machine learning with Pysparkmigrating effortlessly from Pandas and Scikit-Learn /[electronic resource] :by Abdelaziz Testas. - Berkeley, CA :Apress :2023. - xx, 490 p. :illustrations, digital ;24 cm.

Chapter 1: An Easy Transition -- Chapter 2: Selecting Algorithms -- Chapter 3: Multiple Linear Regression with Pandas, Scikit-Learn, and PySpark -- Chapter 4: Decision Trees for Regression with Pandas, Scikit-Learn, and PySpark -- Chapter 5: Random Forests for Regression with Pandas, Scikit-Learn, and PySpark -- Chapter 6: Gradient-Boosted Tree Regression with Pandas, Scikit-Learn and PySpark -- Chapter 7: Logistic Regression with Pandas, Scikit-Learn and PySpark --  Chapter 8: Decision Tree Classification with Pandas, Scikit-Learn and PySpark -- Chapter 9: Random Forest Classification with Scikit-Learn and PySpark -- Chapter 10: Support Vector Machine Classification with Pandas, Scikit-Learn and PySpark -- Chapter 11: Naïve Bayes Classification with Pandas, Scikit-Learn and PySpark -- Chapter 12: Neural Network Classification with Pandas, Scikit-Learn and PySpark -- Chapter 13: Recommender Systems with Pandas, Surprise and PySpark -- Chapter 14: Natural Language Processing with Pandas, Scikit-Learn and PySpark -- Chapter 15: K-Means Clustering with Pandas, Scikit-Learn and PySpark -- Chapter 16: Hyperparameter Tuning with Scikit-Learn and PySpark -- Chapter 17: Pipelines with Scikit-Learn and PySpark -- Chapter 18: Deploying Models in Production with Scikit-Learn and PySpark.  .

Migrate from pandas and scikit-learn to PySpark 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 PySpark offers a roadmap to data scientists considering transitioning from small data libraries (pandas/scikit-learn) to big data processing and machine learning with PySpark. You will learn to translate Python code from pandas/scikit-learn to PySpark 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, Naïve 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 to apply these methods using PySpark, the industry standard for building scalable ML data pipelines. You will: Master the fundamentals of supervised learning, unsupervised learning, NLP, and recommender systems Understand the differences between PySpark, scikit-learn, and pandas Perform linear regression, logistic regression, and decision tree regression with pandas, scikit-learn, and PySpark Distinguish between the pipelines of PySpark and scikit-learn.

ISBN: 9781484297513

Standard No.: 10.1007/978-1-4842-9751-3doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .T47 2023

Dewey Class. No.: 006.31

Distributed machine learning with Pyspark = migrating effortlessly from Pandas and Scikit-Learn /
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245 1 0 $a Distributed machine learning with Pyspark $h [electronic resource] : $b migrating effortlessly from Pandas and Scikit-Learn / $c by Abdelaziz Testas.
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505 0 $a Chapter 1: An Easy Transition -- Chapter 2: Selecting Algorithms -- Chapter 3: Multiple Linear Regression with Pandas, Scikit-Learn, and PySpark -- Chapter 4: Decision Trees for Regression with Pandas, Scikit-Learn, and PySpark -- Chapter 5: Random Forests for Regression with Pandas, Scikit-Learn, and PySpark -- Chapter 6: Gradient-Boosted Tree Regression with Pandas, Scikit-Learn and PySpark -- Chapter 7: Logistic Regression with Pandas, Scikit-Learn and PySpark --  Chapter 8: Decision Tree Classification with Pandas, Scikit-Learn and PySpark -- Chapter 9: Random Forest Classification with Scikit-Learn and PySpark -- Chapter 10: Support Vector Machine Classification with Pandas, Scikit-Learn and PySpark -- Chapter 11: Naïve Bayes Classification with Pandas, Scikit-Learn and PySpark -- Chapter 12: Neural Network Classification with Pandas, Scikit-Learn and PySpark -- Chapter 13: Recommender Systems with Pandas, Surprise and PySpark -- Chapter 14: Natural Language Processing with Pandas, Scikit-Learn and PySpark -- Chapter 15: K-Means Clustering with Pandas, Scikit-Learn and PySpark -- Chapter 16: Hyperparameter Tuning with Scikit-Learn and PySpark -- Chapter 17: Pipelines with Scikit-Learn and PySpark -- Chapter 18: Deploying Models in Production with Scikit-Learn and PySpark.  .
520 $a Migrate from pandas and scikit-learn to PySpark 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 PySpark offers a roadmap to data scientists considering transitioning from small data libraries (pandas/scikit-learn) to big data processing and machine learning with PySpark. You will learn to translate Python code from pandas/scikit-learn to PySpark 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, Naïve 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 to apply these methods using PySpark, the industry standard for building scalable ML data pipelines. You will: Master the fundamentals of supervised learning, unsupervised learning, NLP, and recommender systems Understand the differences between PySpark, scikit-learn, and pandas Perform linear regression, logistic regression, and decision tree regression with pandas, scikit-learn, and PySpark Distinguish between the pipelines of PySpark and scikit-learn.
650 0 $a Machine learning. $3 533906
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650 2 4 $a Machine Learning. $3 3382522
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