Machine Learning with Apache Spark Quick Start Guide: Uncover patterns, derive actionable insights, and learn from big data using MLlib

Quddus, Jillur

ISBN 10: 1789346568 / ISBN 13: 9781789346565

Edité par Packt Publishing, 2018

A propos de cet article

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Détails bibliographiques

• Titre: Machine Learning with Apache Spark Quick Start Guide: Uncover patterns, derive actionable insights, and learn from big data using MLlib
• Éditeur: Packt Publishing
• Date de parution: 2018
• Langue: anglais
• ISBN à 10 chiffres: 1789346568
• ISBN à 13 chiffres: 9781789346565
• Reliure: Couverture souple
• État de l'article: Like New

A propos de ce titre

Synopsis

Combine advanced analytics including Machine Learning, Deep Learning Neural Networks and Natural Language Processing with modern scalable technologies including Apache Spark to derive actionable insights from Big Data in real-time

Key Features:

- Make a hands-on start in the fields of Big Data, Distributed Technologies and Machine Learning

- Learn how to design, develop and interpret the results of common Machine Learning algorithms

- Uncover hidden patterns in your data in order to derive real actionable insights and business value

Book Description:

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.

What You Will Learn:

- Understand how Spark fits in the context of the big data ecosystem

- Understand how to deploy and configure a local development environment using Apache Spark

- Understand how to design supervised and unsupervised learning models

- Build models to perform NLP, deep learning, and cognitive services using Spark ML libraries

- Design real-time machine learning pipelines in Apache Spark

- Become familiar with advanced techniques for processing a large volume of data by applying machine learning algorithms

Who this book is for:

This book is aimed at Business Analysts, Data Analysts and Data Scientists who wish to make a hands-on start in order to take advantage of modern Big Data technologies combined with Advanced Analytics.

À propos de l?auteur

Jillur Quddus is a lead technical architect, polyglot software engineer and data scientist with over 10 years of hands-on experience in architecting and engineering distributed, scalable, high-performance, and secure solutions used to combat serious organized crime, cybercrime, and fraud. Jillur has extensive experience of working within central government, intelligence, law enforcement, and banking, and has worked across the world including in Japan, Singapore, Malaysia, Hong Kong, and New Zealand. Jillur is both the founder of Keisan, a UK-based company specializing in open source distributed technologies and machine learning, and the lead technical architect at Methods, the leading digital transformation partner for the UK public sector.