What is advanced machine learning?

Ongoing advances in computational power (per Moore's Law) have begun to make machine learning, once mostly a research discipline, more viable in commercial contexts. This has caused an explosion of new applications and new or rediscovered techniques, catapulting the obscure concepts of data science, AI, and machine learning into the public consciousness and strategic planning of companies internationally.

The rapid development of machine learning applications is fueled by an ongoing struggle to continually innovate, playing out at an array of research labs. The techniques developed by these pioneers are seeding new application areas and experiencing growing public awareness. While some of the innovations sought in AI and applied machine learning are still elusively far from readiness, others are a reality. Self-driving cars, sophisticated image recognition and altering capability, ever-greater strides in genetics research, and perhaps most pervasively of all, increasingly tailored content in our digital stores, e-mail inboxes, and online lives.

With all of these possibilities and more at the fingertips of the committed data scientist, the profession is seeing a meteoric, if clumsy, growth. Not only are there far more data scientists and AI practitioners now than there were even two years ago (in early 2014), but the accessibility and openness around solutions at the high end of machine learning research has increased.

Research teams at Google and Facebook began to share more and more of their architecture, languages, models, and tools in the hope of seeing them applied and improved on by the growing data scientist population.

The machine learning community matured enough to begin seeing trends as popular algorithms were defined or rediscovered. To put this more accurately, pre-existing trends from a mainly research community began to receive great attention from industry, with one product being a group of machine learning experts straddling industry and academia. Another product, the subject of this section, is a growing awareness of advanced algorithms that can be used to crack the frontier problems of the current day. From month to month, we see new advances made, scores rise, and the frontier moves ever further out.

What all of this means is that there may never have been a better time to move into the field of data science and develop your machine learning skillset. The introductory algorithms (including clustering, regression models, and neural network architectures) and tools are widely covered in web courses and blog content. While the techniques at the cutting edge of data science (including deep learning, semi-supervised algorithms, and ensembles) remain less accessible, the techniques themselves are now available through software libraries in multiple languages. All that's needed is the combination of theoretical knowledge and practical guidance to implement models correctly. That is the requirement that this book was written to address.