Introducing Machine Learning

This is part 1 of a 9 part series on Machine Learning. Our goal is to provide you with a thorough understanding of Machine Learning, different ways it can be applied to your business, and how to begin implementations of Machine Learning within your organization through the assistance of Untitled. This series is not by any means limited to only those with a technical pedigree. The objective is to provide a volume of content that will be informative and practical for a wide array of readers. We hope you enjoy and please do not hesitate to reach out with any questions.

Machine Learning (referred to throughout this series as ML) is one of the most commonly used buzzwords in marketing, and more broadly technology, today. However, beyond some simple terms and ideas, most people don’t really know what Machine Learning is. We’ve noticed that many definitions of Machine Learning are a bit to narrow, such as ML is simply a process to hyper-fit a curve.

Some definitions are also a bit too broad, such as Machine Learning is where statistical learning and advanced computation intersect. Finally, some definitions are just wrong, such as Machine Learning is Artificial Intelligence and the two can be thought of synonymously.

Untitled likes the definition Stanford University uses on their Coursera course: “Machine learning is the science of getting computers to act without being explicitly programmed.” There are a few terms in here to take note of, “science” and “act” in particular.

Machine Learning is a science, first and foremost. With a meeting of applied mathematics, advanced computation, statistics and computer science and theory (in the case of Deep Learning, neuroscience, but more on this is post 5).

Machine Learning really does deserve to be thought of as a field of its own. The second term “act” meaning that Machine Learning behaves, which is why we believe AI and ML are thought of incorrectly as being synonymous. Machine Learning is programmed to behave through the training of Algorithms with training data. Once a given algorithm is trained up, or taught to behave in a certain way by a human, it can begin to produce outputs in a fashion far superior to human computation abilities.

what is machine learning

Machine Learning Application

So what is all the hype really about? Well for starters Machine Learning has vast potential and application. The most advanced companies in the world are in a virtual arms race to produce new Machine Learning recipes and advanced data analytics to compete in the marketplace. Headlines everywhere preach the ML gospel and how it solves all sorts of complex problems for companies ranging from better HR, to how Netflix picks the next movies you’ll enjoy watching, to how autonomous vehicles learn to drive.

One crucial fact remains though, and that is without proper information architecture and good data hygiene, it is very impractical, perhaps even useless, to attempt Machine Learning strategies within your business.

There is a mandatory journey that a company must embark on to reach a point where Machine Learning makes sense to start applying to business problems. The industry often refers to this as the Data Science Hierarchy of needs, which you can find a graphic of here.

As you can see, the first four tiers of the pyramid all convey the need for structured, accurate data and stable infrastructure prior to machine learning being a value add or even possible. You’ll learn much more about why this is throughout this series. The simple answer though as that Machine Learning algorithms are a system, and like any system the saying, “garbage in, garbage out” applies.

This series will take you through a logical introduction to Machine Learning starting with the three types of ML: supervised learning, unsupervised learning and reinforcement learning. We’ll then take a deep dive into deep learning and neural networks.

After that we’ll discuss the differences between ML and AI, how to implement Machine Learning into your marketing strategy, the best open-source ML algorithms that Untitled uses, automated Machine Learning platforms and much more.

If you are interested in starting on a Machine Learning project today, or would like to learn more about how Untitled can assist your company with data analytics strategies and intelligent optimization, please reach out to us through the contact form.

Aaron Peabody

Author Aaron Peabody

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