Machine Learning – What SMBs Need To Know

Discussions related to computing in the current environment have a lot of discussions around machine learning.  What are its implications for an small and medium sized business? Before we try to understand its implications, let us first get a basic background on machine learning in this article.

Machine learning is nothing but an element of AI (Artificial Intelligence) and which refers to the ability of machines to learn, all by themselves. This learning is based upon experiences where a machine observes precise patterns. Here, computers are enabled to not just learn without the need for programming, but also are able to appropriately modify their subsequent actions.

Undoubtedly, machine learning ranks among the most prominent of all contemporary technologies. Yet, whatever has been achieved by the technology, to date, is comparatively less when seen in the light of the things that are possible for it! It is an incontrovertible truth that there consistently are going to be new breakthroughs in the realm, for the next few years.

In the preceding five decades, there is a tremendous surge in data, which literally will be of no use, unless and until the patterns concealed in it are traced. It’s in this connection that machine learning attains special relevance. The technology not just recognizes these patterns, but it also comes out with near-accurate predictions. The fact that this immensely benefits businesses necessitates no special mention.

Key aspects
With machine learning, a huge volume of data can be analyzed and this generates quick results that do not compromise on accuracy. Businesses are empowered to promptly get aware of lucrative prospects as well as perils to be avoided. At this juncture, it won’t be out of place to briefly speak of few patterns of machine learning:

  • Supervised machine learning: In this method, the machine refers back to elements that it has learned in the past, to forecast the subsequent events. In fact, here; the algorithm is enabled to draw comparisons between actual output and anticipated outputs and, promptly identifies errors. The machine then carries out the appropriate modifications.   
  • Unsupervised machine learning: This form of machine learning is useful when projecting outcome is not the priority; the company just wants to categorize. An example of this is: a company intending to divide its clients into various groups, based on their behavior.
  • Reinforcement machine learning: The reinforcement machine learning is relatively uncommon as on date and it is highly complicated, as well. Here, there is periodic negative and positive feedback to machines and with this; the learning ability of the machine gets reinforced.

Now let’s have a look at some terms that are very much in use, in the realm of machine learning:

  • Dataset: Dataset is a collection of many examples of data and which are pivotal to resolving a specific issue.
  • Features: These are several elements of data assisting in rightly comprehending the problem on hand. The features are provided to the pertaining algorithm of machine learning, to simplify the process.
  • Model: By the mention of the term “model”, reference is being made to the phenomenon that has been learned by the given machine. This model can be called as the end result of the algorithm’s learning process.

When you see facial recognition by a smartphone or speak of social media platforms coming out with friend suggestions/ads, the same is attributable to machine learning. Likewise, it is due to this technology that online shopping sites are able to recommend products to you, after making note of your browsing habits.

The purpose of this article was to give those involved in managing of small and medium sized business a simple and easy to understand article on machine learning.