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

Emerging perspectives & drivers for new age data architectures

Driver 1—BIG data intervention.

We have defined big data and large dataset concepts in Chapter 2, Machine learning and Large-scale datasets. The data that is now being ingested and needs to be processed typically has the following characteristics:

  • Source: Depending upon the nature of the information, the source may be a real-time stream of data (for example, trade transactions), or batches of data containing updates since the last sync

  • Content: The data may represent different types of information. Often, this information is related to other pieces of data and is needed to be connected

    The following screenshot shows the types of data and different sources that need to be supported:

  • Volume: Depending upon the nature of the data, the volumes that are being processed may vary. For example, master data or the securities definition data are relatively fixed, whereas the transaction data is enormous compared to the other two.

  • Lifecycle...