Machine Learning with PySpark
With Natural Language Processing and Recommender Systems
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Product details:
- Edition number 1st ed.
- Publisher Springer, Berlin
- Date of Publication 1 January 2018
- Number of Volumes 1 pieces, Book
- ISBN 9781484241301
- Binding Paperback
- No. of pages223 pages
- Size 234x156x13 mm
- Weight 402 g
- Language English
- Illustrations 1 Farbabb., 149 SW-Abb. Illustrations, black & white 0
Categories
Short description:
- Build a spectrum of supervised and unsupervised machine learning algorithms
- Implement machine learning algorithms with Spark MLlib libraries
- Develop a recommender system with Spark MLlib libraries
- Handle issues related to feature engineering, class balance, bias and variance, and cross validation for building an optimal fit model
Long description:
Build machine learning models, natural language processing applications, and recommender systems with PySpark to solve various business challenges. This book starts with the fundamentals of Spark and its evolution and then covers the entire spectrum of traditional machine learning algorithms along with natural language processing and recommender systems using PySpark.
Machine Learning with PySpark shows you how to build supervised machine learning models such as linear regression, logistic regression, decision trees, and random forest. You'll also see unsupervised machine learning models such as K-means and hierarchical clustering. A major portion of the book focuses on feature engineering to create useful features with PySpark to train the machine learning models. The natural language processing section covers text processing, text mining, and embedding for classification.
After reading this book, you will understand how to use PySpark's machine learning library to build and train various machine learning models. Additionally you'll become comfortable with related PySpark components, such as data ingestion, data processing, and data analysis, that you can use to develop data-driven intelligent applications.
What You Will Learn
- Build a spectrum of supervised and unsupervised machine learning algorithms
- Implement machine learning algorithms with Spark MLlib libraries
- Develop a recommender system with Spark MLlib libraries
- Handle issues related to feature engineering, class balance, bias and variance, and cross validation for building an optimal fit model
Who This Book Is For
Data science and machine learning professionals.
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Table of Contents:
Chapter 1: Evolution of DataChapter 2: Introduction to Machine LearningChapter 3: Data ProcessingChapter 4: Linear RegressionChapter 5: Logistic RegressionChapter 6: Random ForestsChapter 7: Recommender SystemsChapter 8: ClusteringChapter 9: Natural Language Processing
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