Machine Learning with PySpark
With Natural Language Processing and Recommender Systems
|Price||$20.41 - $28.15
|Format||Paper book / ebook (PDF)|
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.
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.
by Brett Lantz
Updated and upgraded to the latest libraries and most modern thinking, Machine Learning with R, Second Edition provides you with a rigorous introduction to this essential skill of professional data science. Without shying away from technical theory, it is written to provide focused and practical knowledge to get you building algorithms an...
Price: $34.99 | Publisher: Packt Publishing | Release: 2015
by Thomas K Abraham, Parashar Shah, Jen Stirrup, Lauri Lehman, Anindita Basak
Implementing Machine learning (ML) and Artificial Intelligence (AI) in the cloud had not been possible earlier due to the lack of processing power and storage. However, Azure has created ML and AI services that are easy to implement in the cloud. Hands-On Machine Learning with Azure teaches you how to perform advanced ML projects in the c...
Price: $36.50 | Publisher: Packt Publishing | Release: 2018
by Joshua Newnham
Core ML is a popular framework by Apple, with APIs designed to support various machine learning tasks. It allows you to train your machine learning models and then integrate them into your iOS apps.Machine Learning with Core ML is a fun and practical guide that not only demystifies Core ML but also sheds light on machine learning. In this...
Price: $39.99 | Publisher: Packt Publishing | Release: 2018
by Matt R. Cole
The necessity for machine learning is everywhere, and most production enterprise applications are written in C# using tools such as Visual Studio, SQL Server, and Microsoft Azur2e. Hands-On Machine Learning with C# uniquely blends together an understanding of various machine learning concepts, techniques of machine learning, and various a...
Price: $31.99 | Publisher: Packt Publishing | Release: 2018
by Burak Kanber
Price: $35.99 | Publisher: Packt Publishing | Release: 2018
by Jeffrey Jackovich, Ruze Richards
Machine Learning with AWS is the right place to start if you are a beginner interested in learning useful artificial intelligence (AI) and machine learning skills using Amazon Web Services (AWS), the most popular and powerful cloud platform. You will learn how to use AWS to transform your projects into apps that work at high speed and are...
Price: $34.99 | Publisher: Packt Publishing | Release: 2018
by Nick Pentreath
Apache Spark is a framework for distributed computing that is designed from the ground up to be optimized for low latency tasks and in-memory data storage. It is one of the few frameworks for parallel computing that combines speed, scalability, in-memory processing, and fault tolerance with ease of programming and a flexible, expressive, ...
Price: $29.99 | Publisher: Packt Publishing | Release: 2015
by Sudipta Mukherjee
The F# functional programming language enables developers to write simple code to solve complex problems. With F#, developers create consistent and predictable programs that are easier to test and reuse, simpler to parallelize, and are less prone to bugs.If you want to learn how to use F# to build machine learning systems, then this is th...
Price: $31.99 | Publisher: Packt Publishing | Release: 2016