Distributed Machine Learning with PySpark : Migrating Effortlessly from Pandas and Scikit-Learn / by Abdelaziz Testas.

Format:

Book

Author/Creator:

Testas, Abdelaziz.

Series:

Professional and Applied Computing Series

Language:

English

Subjects (All):

Artificial intelligence.

Machine learning.

Python (Computer program language).

Artificial Intelligence.

Machine Learning.

Python.

Local Subjects:

Artificial Intelligence.

Machine Learning.

Python.

Physical Description:

1 online resource (500 pages)

Edition:

1st ed. 2023.

Place of Publication:

Berkeley, CA : Apress : Imprint: Apress, 2023.

Summary:

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.

Contents:

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. .

Notes:

Naive Bayes with Scikit-Learn

Description based on publisher supplied metadata and other sources.

ISBN:

9781484297513

1484297512

OCLC:

1411311762