E-Books →Machine Learning with PySpark With Natural Language Processing and Recommender Systems

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 LearnBuild a spectrum of supervised and unsupervised machine learning algorithmsImplement machine learning algorithms with Spark MLlib librariesDevelop a recommender system with Spark MLlib librariesHandle 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.