Overview

Machine Learning (ML), which is a subset of Artificial intelligence (AI), enhances the ability of a computer to learn, from data, without being explicitly programmed end-to-end. As ML and AI learn they acquire the ability to carry out cognitive functions, such as perceiving, learning, reasoning and automatically digging deeper to identify important insights or new novel discovery. With the advance in machine learning, in particular its Deep Learning (DL) subset, ML is rapidly spreading across sectors and will continue to do so at an even higher rate with the ever increasing growth of Big Data. Gartner predicts that companies will combine Big Data and Machine Learning to carry out some or most of their service processes by 40% in 2022, up from 5% in 2017. ML is used to accelerate data-driven discovery in research and development. Recently, it has enabled scientists to discover largely unknown diversity of viruses, amounting to thousands of previously unknown viruses. The book refers to previous as well recent research work, with colleagues, where ML was used to capture subtle variation and to discover rare items, such as rare genes which researchers have so long sought for in vain. Such processes to identify genes or medicine can be daunting, as it may take years and can be expensive and the outcome can be uncertain. ML is used today to shorten the time and even help to identify medicine that can be more effective for people with a particular gene, which will help in turn in personalized medicine. ML is a critical ingredient for intelligent applications and provides the opportunity to further accelerate discovery processes as well as enhancing decision making processes. These trends promise that every sector will be data-driven and will be using machine learning in the cloud to incorporate artificial intelligence applications and to ultimately supplement existing analytical and decision making tools. The book introduces ML and its potential along with some ML applications using Spark and R platforms combined. While Spark has the possibility to scale and speed up analytics, it harness R language's machine learning capabilities beyond what is available on Spark or any other Big Data system. R and Spark can share codes and different types of data and carry out powerful large scale machine learning capabilities. Machine learning with Spark and R language combined can not only speed up but also light up Big Data Discovery. The book contains 10 chapters, the first chapter highlights ML quests, chapter 2 provides a detailed historical perspective, chapter 3 shows how ML works by introducing conceptual frameworks of ML, chapter 4 lists some of the metrics used to assess the performance of ML types. Chapter 5, 6 and 7 focus on different types of ML including supervised, unsupervised and reinforced learning. Chapter 8 and chapter 9 introduce the ML implementation platforms of R and Spark with their different libraries including Spark MLlib. Chapter 10 provides different walk-through ML examples using both R and Spark ML techniques.

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