Fundamentals of Machine Learning
In the last few years, Machine Learning has quickly gone from a niche subject to one with significant relevance to many companies and organizations. Across industries ranging from pharmaceuticals and healthcare, to retail and financial services, Machine Learning has become more widely used for solving new business requirements. But just what is Machine Learning and how does it work? Just how do you teach a machine to learn?
Machine Learning at its most basic is the practice of using algorithms to parse large volumes of data, learn from it, and then make a determination or prediction about something in the world. We can work out the probability of certain events occurring in a specific way, and these values changes as more and more events actually happen. This can then affect the likelihood, or probability, of the next event occurring in that way. So rather than hand-coding software routines with a specific set of instructions to accomplish a particular task, the machine is “trained” using large amounts of data and algorithms that give it the ability to learn how to perform the task. This can be expanded to more and more complicated systems with enough data, i.e. enough previous events having happened, then we can make more and more accurate predictions about the likelihood of a future event.
Watch the Webinar on Demand and get an overview of the following methods:
– Supervised Learning
– Unsupervised Learning
– Reinforcement Learning
This session is an Intermediate level talk. It is geared towards Architects, Data Scientists, Developers, Software Engineers and anyone with an interest in Data Matching.