A Comprehensive Guide to Machine Learning
Machine Learning is a term thrown around in technology circles with an ever-increasing intensity. Major technology companies have attached themselves to this buzzword to receive capital investments, and every major technology company is pushing its even shinier parent artificial intelligence (AI).
The reality is that Machine Learning as a concept is as old as computing itself. As early as 1950, Alan Turing was asking the question, “Can computers think?” In 1969, Arthur Samuel helped define Machine Learning specifically by stating, “[Machine Learning is the] field of study that gives computers the ability to learn without being explicitly programmed.”
While the concept has been around for more than half a century, we have finally reached a point in technological advancement where hardware and software can actually help developers match their aspirations with tangible reality. This development has led to not only the rise of Machine Learning and AI advancements, but, more importantly, also advancements inexpensive enough for anyone to use.
While the topic of Machine Learning and AI has been exhaustively covered in the technology space, a singular comprehensive guide has not been created in the marketing space on the topic, including how it affects marketers and their work. This space is so thick with technology-based terminology, but not every marketer has the chops to venture into the space with confidence. With many products coming to market, iPullRank believes that preparing marketers to tackle the landscape armed with a solid foundation is important.
Since Machine Learning touches an ever increasing number of industries, we’ll also touch on several different ways that Machine Learning is impacting people in many professions. Most data scientists use R and Python for Machine Learning, but have you met a marketer these days that only lives and breathes data science? We created this guide for the marketers among us whom we know and love by giving them simpler tools that don’t require coding for Machine Learning.
We’ll briefly touch on other spaces to round out marketers’ understanding of the complex topic we’re tackling. We’ll also look at how Machine Learning can help marketers through examination of use cases, plus we’ll dive into martech, a field that’s increasingly including machine-learning concepts.
Since we want to create the most comprehensive resource on Machine Learning for marketers, this guide will be far more than explanatory. We’ll be looking at relevant use cases related to Machine Learning and delving into practical use of machine learning so that you can begin to use the technologies we’ll discuss after you read this guide.