Details
Machine Learning with PySpark
With Natural Language Processing and Recommender Systems|
34,99 € |
|
| Verlag: | Apress |
| Format: | |
| Veröffentl.: | 14.12.2018 |
| ISBN/EAN: | 9781484241318 |
| Sprache: | englisch |
Dieses eBook enthält ein Wasserzeichen.
Beschreibungen
<div><div><div><div><div>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. </div><div><br></div><div><i>Machine Learning with PySpark</i> 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. </div><div><br></div><div>After reading thisbook, 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.</div><br></div><div><b>What You Will Learn</b></div><div><ul><li>Build a spectrum of supervised and unsupervised machine learning algorithms<br></li><li>Implement machine learning algorithms with Spark MLlib libraries<br></li><li>Develop a recommender system with Spark MLlib libraries<br></li><li>Handle issues related to feature engineering, class balance, bias and variance, and cross validation for building an optimal fit model</li></ul></div><div><br></div><div><b>Who This Book Is For </b></div><div><br></div><div>Data science and machine learning professionals. </div><div><br></div><div><br></div></div></div></div>
<div><p>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.</p><br></div>
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.
<div><div><div>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. </div><div><br></div><div><i>Machine Learning with PySpark</i> 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. </div><div><br></div><div>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.</div><br></div><div>You will:</div><div><ul><li>Build a spectrum of supervised and unsupervised machine learning algorithms<br></li><li>Implement machine learning algorithms with Spark MLlib libraries<br></li><li>Develop a recommender system with Spark MLlib libraries<br></li><li>Handle issues related to feature engineering, class balance, bias and variance, and cross validation for building an optimal fit model</li></ul></div></div>
Covers all PySpark machine learning models including PySpark advanced methods Contains practical applications of machine learning algorithms Presents advanced features of engineering techniques for machine learning models
Diese Produkte könnten Sie auch interessieren:
