Machine Learning with PySpark
With Natural Language Processing and Recommender Systems
Seiten
2018
|
1st ed.
Apress (Verlag)
978-1-4842-4130-1 (ISBN)
Apress (Verlag)
978-1-4842-4130-1 (ISBN)
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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.
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.
Pramod Singh is an established data scientist with over eight years of experience in data and solving business challenges. He has worked in organizations such as Infosys, Tally and SapientRazorfish. Also, president of a data science meet-up group and regular speaker at various webinars. Recently spoke at major conference: GIDS 2018 and presented a session on “Sequence Embedding in Spark” which was well received. He has an online Udemy course on machine learning.
Chapter 1: Evolution of Data
Chapter 2: Introduction to Machine Learning
Chapter 3: Data Processing
Chapter 4: Linear Regression
Chapter 5: Logistic Regression
Chapter 6: Random Forests
Chapter 7: Recommender Systems
Chapter 8: Clustering
Chapter 9: Natural Language Processing
| Erscheinungsdatum | 11.01.2019 |
|---|---|
| Zusatzinfo | 1 Illustrations, color; 149 Illustrations, black and white; XVIII, 223 p. 150 illus., 1 illus. in color. |
| Verlagsort | Berkley |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 454 g |
| Themenwelt | Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge |
| Mathematik / Informatik ► Informatik ► Software Entwicklung | |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Mathematik / Informatik ► Mathematik ► Finanz- / Wirtschaftsmathematik | |
| Wirtschaft ► Allgemeines / Lexika | |
| Schlagworte | machine learning • PySpark • Python • Recommender Systems • Reinforcement Learning • supervised learning • Unsurpervised Learning |
| ISBN-10 | 1-4842-4130-4 / 1484241304 |
| ISBN-13 | 978-1-4842-4130-1 / 9781484241301 |
| Zustand | Neuware |
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