There are numerous real-world use cases where the number of features available that may potentially be used to train a model is very large. A common example is economic data, and using its constituent stock price data, employment data, banking data, industrial data, and housing data together to predict the gross domestic product (GDP). Such types of data are said to have high dimensionality. Though they offer numerous features that can be used to model a given use case, high-dimensional datasets increase the computational complexity of machine learning algorithms, and more importantly may also result in over fitting. Over fitting is one of the results of the curse of dimensionality, which formally describes the problem of analyzing data in high-dimensional spaces (which means that the data may contain many attributes, typically hundreds or even thousands...
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Machine Learning with Apache Spark Quick Start Guide
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Machine Learning with Apache Spark Quick Start Guide
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Overview of this book
Every person and every organization in the world manages data, whether they realize it or not. Data is used to describe the world around us and can be used for almost any purpose, from analyzing consumer habits to fighting disease and serious organized crime. Ultimately, we manage data in order to derive value from it, and many organizations around the world have traditionally invested in technology to help process their data faster and more efficiently.
But we now live in an interconnected world driven by mass data creation and consumption where data is no longer rows and columns restricted to a spreadsheet, but an organic and evolving asset in its own right. With this realization comes major challenges for organizations: how do we manage the sheer size of data being created every second (think not only spreadsheets and databases, but also social media posts, images, videos, music, blogs and so on)? And once we can manage all of this data, how do we derive real value from it?
The focus of Machine Learning with Apache Spark is to help us answer these questions in a hands-on manner. We introduce the latest scalable technologies to help us manage and process big data. We then introduce advanced analytical algorithms applied to real-world use cases in order to uncover patterns, derive actionable insights, and learn from this big data.
Table of Contents (10 chapters)
Preface
Free Chapter
The Big Data Ecosystem
Setting Up a Local Development Environment
Artificial Intelligence and Machine Learning
Supervised Learning Using Apache Spark
Unsupervised Learning Using Apache Spark
Natural Language Processing Using Apache Spark
Deep Learning Using Apache Spark
Real-Time Machine Learning Using Apache Spark
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