ABSTRACT
The objective of this chapter is to provide a concise view on machine learning (ML) for big data analytics and to enlighten the reader about interactive reinforcement ML. The big data revolution promises to transform life, work, and thought by enabling process optimization, empowering insight discovery, and improving decision-making. It describes the ability to extract value from massive amounts of data through data analytics. This chapter compiles, summarizes, and organizes ML challenges with big data. It highlights the cause–effect relationship by organizing challenges according to the “big data Vs,” the dimensions that instigated the issue: volume, velocity, variety, or veracity. It also describes how reinforcement learning allows machines to work automatically. Moreover, emerging ML approaches and techniques are discussed in terms of how they are capable of handling the various challenges with the ultimate objective of helping practitioner's select appropriate solutions for their use cases. Finally, a matrix relating the challenges and approaches is presented. Through this process this chapter provides a perspective on the domain, identifies research gaps and opportunities, and provides a strong foundation and encouragement in the field of ML with big data.
