Book Image

Practical Machine Learning

By : Sunila Gollapudi
Book Image

Practical Machine Learning

By: Sunila Gollapudi

Overview of this book

This book explores an extensive range of machine learning techniques uncovering hidden tricks and tips for several types of data using practical and real-world examples. While machine learning can be highly theoretical, this book offers a refreshing hands-on approach without losing sight of the underlying principles. Inside, a full exploration of the various algorithms gives you high-quality guidance so you can begin to see just how effective machine learning is at tackling contemporary challenges of big data This is the only book you need to implement a whole suite of open source tools, frameworks, and languages in machine learning. We will cover the leading data science languages, Python and R, and the underrated but powerful Julia, as well as a range of other big data platforms including Spark, Hadoop, and Mahout. Practical Machine Learning is an essential resource for the modern data scientists who want to get to grips with its real-world application. With this book, you will not only learn the fundamentals of machine learning but dive deep into the complexities of real world data before moving on to using Hadoop and its wider ecosystem of tools to process and manage your structured and unstructured data. You will explore different machine learning techniques for both supervised and unsupervised learning; from decision trees to Naïve Bayes classifiers and linear and clustering methods, you will learn strategies for a truly advanced approach to the statistical analysis of data. The book also explores the cutting-edge advancements in machine learning, with worked examples and guidance on deep learning and reinforcement learning, providing you with practical demonstrations and samples that help take the theory–and mystery–out of even the most advanced machine learning methodologies.
Table of Contents (23 chapters)
Practical Machine Learning
About the Author
About the Reviewers

Modern data architectures for Machine learning

From this section onwards, we will cover some of the emergent data architectures, challenges that gave rise to architectures of this implementation architecture, some relevant technology stacks, and use cases where these architectures apply (as relevant) in detail.

Semantic data architecture

Some of the facts covered in the emerging perspectives in the previous section give rise to the following core architecture drivers to build semantic data model driven data lakes that seamlessly integrate a larger data scope, which is analytics ready. The future of analytics is semantified. The goal here is to create a large-scale, flexible, standards-driven ETL architecture framework that models with the help of tools and other architecture assets to enable the following:

  • Enabling a common data architecture that can be a standard architecture.

  • Dovetailing into the Ontology-driven data architecture and data lakes of the future (it is important to tie this architecture...