Working in the IT field for almost 20 years has afforded me the ability to focus on many different areas and to gain a wealth of experience in many generalities. I would say that I have never been a specialist in anything, other than perhaps network performance monitoring and managing large-scale enterprise software packages. This is not an extremely difficult task, in and of itself, so I have always been interested in gaining some new experience that I can specialize in. The new role I find myself in is engineering, but from a sales slant. That means that I spend more time cultivating relationships and less time on the keyboard, so I spend much of my personal time and most of my work time doing research and actively developing in an effort not to lose my edge. Recently, I was sent out to one of our offices for some training in “Business Analytics and IOT”. For the uninitiated, IOT is the acronym for Internet of Things, which refers to all of the various Internet-connected devices which include so much more these days than personal computers and large company mainframes.
I will begin by saying that I was never a great student. I hated school and I was never one to put extra effort into my studies. As an adult, I can see how that has precluded me from being better at things I want to be, but I fight now to gain the knowledge I missed in school so that I can be proficient in those weakest areas. One such area is mathematics. This affects my ability to understand complex algorithms used in areas such as encryption. Working in the IT field, one it surrounded with a myriad of uses cases where encryption is utilized, and while most people do not need to comprehend the inner workings of algorithms used there, I would like to better understand what is happening to my data as it passed through this “black box”. Never have I wished I had learned more in school related to mathematics, than during this training at our office in Seattle last week.
Business analytics and monitoring of IOT devices requires something that is becoming quite the buzz word these days, machine learning. Now, when I say machine learning, I am not referring to artificial intelligence, or AI. These can be mutually exclusive subjects. I can guarantee that I will be providing more on this subject matter in the coming months as I begin to delve into the subject more deeply. I find machine learning to be a very interesting focus area because what I mean when I refer to machine learning is:
Machine learning is the subfield of computer science that gives computers the ability to learn without being explicitly programmed. – from the wise and mysterious Wikipedia
During this training class with my company’s internal specialists on data science and machine learning, I was introduced to machine learning for the first time. I was afraid to participate in the classes when I learned how heavily the classes relied on a foundation data science. I realized throughout the week that I actually had a much easier time comprehending the algorithms most often used in data science and how they could be applied within the product we sell, but also in general terms. I would like to provide more on this subject soon so that others may have the same awakening I had during this training. We will call this an intro to machine learning and build a series on this topic. I hope you will provide comments and suggestions that might make this more fun and enjoyable, yet challenging to our readers.
The video above is provided by the Stanford School of Engineering and is a basic introduction to machine learning. I will include these in my future posts as well, although I do not expect them to align perfectly with each post in the series. I want to make sure people are aware of this resource as we move forward. In the next post in the series, we will be looking at a couple of specific algorithms used in machine learning and we will also have a conversation about supervised and unsupervised learning.