Machine Learning – this term has been doing the rounds for quite some time now. This name was coined by AI enthusiast and American computer gaming pioneer Arthur Samuel. Gone are the days when a computer was just meant to type out pages and insert data into tables or play 8-bit computer games. In today’s world, a computer is essentially present in almost each and every electronic device that we see around us. Machine Learning is the next big step in this direction of connecting each and every node of our life and making a gigantic network that will help us out a lot in day to day common activities.
What it is: Imagine a child. Now what you do is that you provide the child with a few small and easy to understand steps that forms the basis of almost each and every thing that he will be learning from then on. Now the child, as lazy as we humans generally are, will try to look out for efficient solutions from the steps given to him. This will help him in analyzing conditions and scenarios, detect potential solutions and from them carry out the most feasible and efficient one. Now apply this same procedure in case of a machine or a computer to be more specific and what you get is the process of machine learning.
Theory: As simple as it sounds, machine learning in theory is very different from the practical example given above. And, not to mention difficult. The main and one and only focus is to learn from experiences and generalize context to fully understand the scene and then take appropriate action.
Approaches: There are many different strategies and approaches that one can take. Some of the most important approaches that one can take include the Decision Tree Learning, the Association Rule Learning, Deep Learning and so on. Artificial Neural Networks and Inductive Logic programming and so on and so forth are important. Applications are thus wide and vast, and so are the endless possibilities of advancements that it brings with itself.
As good as the prospect sounds, it is important that we deal with such technologies very cautiously. For instance, just like in the case of AI and IoT, each of these has two sides – the good and the bad, and each comes with the other. How we put it to use and what we use it for is what needs to be considered and controlled thoroughly.