Machine learning was conceptualized long back and over the years it has evolved to be a promising solution. The past couple of years have witnessed the rise of machine learning as a solution that is revolutionary for almost all major sectors.
It can be defined as a technological advancement where computers are able to decode patterns and trends from big data providing information that can be a stepping stone to achieving big things in businesses. This blog aims to provide details of the types of machine learning, the techniques involved, etc. The blog will also offer a glimpse into what future holds for machine learning.
The Versatile Types
Machine learning is about codes and algorithms that are used to perform certain functions. There is a level of flexibility when we talk about these algorithms. It is not necessarily required to perform functions using label, you can let the systems identify patterns without your intervention in case you have unlabelled data.
The data that is required for machine learning to function can be structured or unstructured. And, this is one of the many reasons that might require you to select a type of machine learning. In simple terms, machine learning can be:
- Supervised: In this case, algorithms are used to identify trends based on your expectation. For example, there is a pool of data that has information of customers that a company has served in the past 5 years. Now, I am looking for data of only female customers under 35 years of age who have shopped for cosmetics. In this case, using supervised machine learning, I can use codes to take out data of only customers that match the criteria.
- Unsupervised: This is a little complex form of machine learning. In this case, patterns or trends are identified on the basis of experience. For example, when you select e-mails from a certain user as spam, the system registers that and after a particular number of people report that mail as spam, the mails from that user reaches the spam folder. In this case, no pre-set condition existed, the machine learnt from what you did.
These are the two dominant categories and with passing time there are more versions like blended machine learning where features of both supervised and unsupervised types are used.
Machine learning is not everything good, it has its set of drawbacks as well. The first one is that it is useful only when you are dealing with big data. There is no point in investing a huge sum in acquired machine learning solutions that will deliver results that an excel sheet can when you are dealing with data is small in size. If you are a small-scale firm, then sticking to traditional approaches is more feasible.
Secondly, machine learning is prone to mistakes. While the Software would not make error is calculations and computations, the data that is used can make outcomes inconsistent. This means that if you are dealing with a data set that is inaccurate or biased, then the outcomes will reflect that and using that outcome to make decisions is not suggested.
Machine learning has successfully paved way for itself in the future solutions. Most of the solutions that are available in the market today to facilitate your business operation use machine learning in some form or another. An example can be popular customer relationship management solutions that are being widely publicized and adopted. Acting as a cherry on the cake is the fact, that major players like Google and Amazon are banking on machine learning as a future technology.
With all the machine learning has to offer, it would not be incorrect to assume that it is the technology of the present and the future. Let us know what you think by sharing your views on machine learning and its future prospects.
Author Bio: Author of this article works for a software application development company, providing custom software solutions. Stay connected to him on Facebook and Twitter.