Book Image

Machine Learning with Apache Spark Quick Start Guide

By : Jillur Quddus
Book Image

Machine Learning with Apache Spark Quick Start Guide

By: Jillur Quddus

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)

NLP

NLP refers to a family of computer science disciplines, including machine learning, linguistics, information engineering, and data management, used to analyze and understand natural languages, including speech and text. NLP can be applied to a wide variety of real-world use cases, including the following:

  • Named entity recognition (NER): Automatically identifying and parsing entities from text, including people, physical addresses, and email addresses
  • Relationship extraction: Automatically identifying the types of relationships between parsed entities
  • Machine translation and transcription: Automatically translating from one natural language to another, for example, from English to Chinese
  • Searching: Automatically searching across vast collections of structured, semi-structured, and unstructured documents and objects in order to fulfill a natural language query
  • Speech recognition...