Chapter 1. Machine Learning Review

Recent years have seen the revival of artificial intelligence (AI) and machine learning in particular, both in academic circles and the industry. In the last decade, AI has seen dramatic successes that eluded practitioners in the intervening years since the original promise of the field gave way to relative decline until its re-emergence in the last few years.

What made these successes possible, in large part, was the impetus provided by the need to process the prodigious amounts of ever-growing data, key algorithmic advances by dogged researchers in deep learning, and the inexorable increase in raw computational power driven by Moore's Law. Among the areas of AI leading the resurgence, machine learning has seen spectacular developments, and continues to find the widest applicability in an array of domains. The use of machine learning to help in complex decision making at the highest levels of business and, at the same time, its enormous success in improving the accuracy of what are now everyday applications, such as searches, speech recognition, and personal assistants on mobile phones, have made its effects commonplace in the family room and the board room alike. Articles breathlessly extolling the power of deep learning can be found today not only in the popular science and technology press but also in mainstream outlets such as The New York Times and The Huffington Post. Machine learning has indeed become ubiquitous in a relatively short time.

An ordinary user encounters machine learning in many ways in their day-to-day activities. Most e-mail providers, including Yahoo and Gmail, give the user automated sorting and categorization of e-mails into headings such as Spam, Junk, Promotions, and so on, which is made possible using text mining, a branch of machine learning. When shopping online for products on e-commerce websites, such as https://www.amazon.com/, or watching movies from content providers, such as Netflix, one is offered recommendations for other products and content by so-called recommender systems, another branch of machine learning, as an effective way to retain customers.

Forecasting the weather, estimating real estate prices, predicting voter turnout, and even election results—all use some form of machine learning to see into the future, as it were.

The ever-growing availability of data and the promise of systems that can enrich our lives by learning from that data place a growing demand on the skills of the limited workforce of professionals in the field of data science. This demand is particularly acute for well-trained experts who know their way around the landscape of machine learning techniques in the more popular languages, such as Java, Python, R, and increasingly, Scala. Fortunately, thanks to the thousands of contributors in the open source community, each of these languages has a rich and rapidly growing set of libraries, frameworks, and tutorials that make state-of-the-art techniques accessible to anyone with an internet connection and a computer, for the most part. Java is an important vehicle for this spread of tools and technology, especially in large-scale machine learning projects, owing to its maturity and stability in enterprise-level deployments and the portable JVM platform, not to mention the legions of professional programmers who have adopted it over the years. Consequently, mastery of the skills so lacking in the workforce today will put any aspiring professional with a desire to enter the field at a distinct advantage in the marketplace.

Perhaps you already apply machine learning techniques in your professional work, or maybe you simply have a hobbyist's interest in the subject. If you're reading this, it's likely you can already bend Java to your will, no problem, but now you feel you're ready to dig deeper and learn how to use the best of breed open source ML Java frameworks in your next data science project. If that is indeed you, how fortuitous is it that the chapters in this book are designed to do all that and more!

Mastery of a subject, especially one that has such obvious applicability as machine learning, requires more than an understanding of its core concepts and familiarity with its mathematical underpinnings. Unlike an introductory treatment of the subject, a book that purports to help you master the subject must be heavily focused on practical aspects in addition to introducing more advanced topics that would have stretched the scope of the introductory material. To warm up before we embark on sharpening our skills, we will devote this chapter to a quick review of what we already know. For the ambitious novice with little or no prior exposure to the subject (who is nevertheless determined to get the fullest benefit from this book), here's our advice: make sure you do not skip the rest of this chapter; instead, use it as a springboard to explore unfamiliar concepts in more depth. Seek out external resources as necessary. Wikipedia them. Then jump right back in.

For the rest of this chapter, we will review the following:

• History and definitions
• What is not machine learning?
• Concepts and terminology
• Important branches of machine learning
• Different data types in machine learning
• Applications of machine learning
• Issues faced in machine learning
• The meta-process used in most machine learning projects
• Information on some well-known tools, APIs, and resources that we will employ in this book