You have definitely heard something about machine learning, and you are also vaguely aware that it could massively contribute to your business operations. Were you aware, though, that even for a beginner such as yourself, it is not as painfully difficult to implement a machine learning model as you might expect?
Machine learning, to introduce the concept cleanly, is the scientific process or programming computers to learn, and then act, as a human would. This means improving the learning process over a period of time and in a way that is autonomous of human intervention. It can be achieved with the presentation of significant amounts of relevant data based on real-world interactions.
As is to be expected, when it comes to machine learning algorithms, there are numerous possibilities, and they are usually divided by learning style (unsupervised leaning could be a possibility), or perhaps by the function of the operation (classification, for example). Yet every algorithm consists of the same components, namely representation, meaning the classifiers that the computer understands, evaluation, and then optimization.
“It may not necessarily be simple, but developing a machine learning system using these components is not as unrealistic as you may expect, and enterprises of all types and sizes can utilize this approach. It plainly involves following the steps that need to be followed in development,” says Michael F. Grogan, a tech writer at Britstudent and 1Day2write.
Here are those steps:
Define appropriately the required objective
It all starts with an objective, as it does with so many business development activities across multiple spheres. If you want to improve operations, you first need to identify how this can be achieved, and then set your target. Whatever your business, you will conceivably want to improve customer experience and satisfaction at the same time, and add value across your business operations.
Acquire and explore data
Next up is the vitally important step of gathering all the data that will be analyzed for the machine learning model. This is no easy task, and the inclusion of relevant data sets is an integral aspect of the machine learning approach actually delivering towards your desired outcome.
Choose a measure of success
This is a crucial point. How will you define what success looks like in your model? And how to you measure that success? As Harvard professor Peter Drucker says, “If you can’t measure it, you can’t improve it”.
Set an evaluation protocol
“When you know how you will measure your definition of success, you then need a protocol for evaluation purposes. There are differing options here, including maintaining what is known as a hold out validation set, utilizing K-fold validation, or an iterated version of K-fold validation,” advises Carmel King, an IT specialist at Writemyx and Nextcoursework.
Prototype and test
Then you need to construct a prototype machine that will perform these tasks. It must be tested, and iterated until you have a product ready to be used.
Acquiring great data is not the end of this process by any means. There is a lot of work still to be performed in order to test algorithms, ascertain the relevant features, and perform trial-and-error experiments.
One of the more frustrating aspects of the prototyping and testing period is that it’s very difficult to predict exactly how long this will take, as results can vary widely. The validation process in itself, where model performance assessed on your pre-defined metrics will be carried out, can be a frustrating element, but crucial, nonetheless.
Keep an eye out for clustering, and gaps in your produced results. Often simple observations can be good enough to tell you this, although a more stringent approach is recommended.
This is the happy stage where you can say that the developed product is good enough to be rolled out across the business. Here you need to think about scaling requirements, and of course the key element of accessibility. Which users will get initial access, and how will you roll that out? You are never done, because the product itself will probably need to be scaled up and evolved, considering outliers (those results who users whose needs are not addressed accurately by the product) and increasing the portion of data that you cover.
This summary works as a conceptual flow of the machine learning modeling process. Moving from one step to the next is not always final, but it works as an effective overview of the model as a whole.
By Michael Dehoyos, a content marketer and editor at PhdKingdom.com and Academicbrits.com, where he works with companies to develop their marketing strategies. Michael also contributes to numerous sites and publications and is a writer at OriginWritings.com.