What is machine learning all about?
Machine learning is a process by which the machines are able to improvise upon the tasks which they are already carrying out and take intelligent decisions regarding the same. As an analogy, we can imagine the robotic arm which learns over a period of time to acquire grips over different objects differently in an intelligent manner. This can be envisioned from the fact that the grip to hold a glass of water may be different from the grip to hold a flower vase as both objects have different curvature. If you are wondering how does a robotic arm learn to grip different objects differently, then the answer is probably machine learning.
Different types of machine learning
Machine learning can be supervised, unsupervised or reinforced. To explain these terms briefly let us first take the process of supervised learning. We may think of supervised learning as a new student being administered lessons under the guidance of a teacher.
Technically speaking, a machine in supervised learning is fed with labelled data so that the output generated is rather a controlled one. The input is a training data set which does the work of a teacher as is visible from the analogy.
When we talk about unsupervised learning, we may in a simple case imagine a student who decides to learn not from his teacher but on his own.
In formal terms, unsupervised learning may be defined as the learning of a machine with the help of an input which is unlabelled. This means that unlabelled data is used as training set and the machine is allowed to discover various hidden patterns and that too on its own.
When it comes to reinforcement learning, we can think of it in terms of a very interesting analogy. Imagine a person who is trapped on an island. In this case, he will have to interact effectively with the environment and use this input to train himself. The trial and error method is used in such a case to learn directly from the environment. Hence, when a machine is exposed to reinforcement learning, it learns unconditionally from the environment over a period of time.
Time span to get equipped with machine learning
The subject of machine learning is not so complex that it would take years to develop an adequate know how about it. However, it is not as easy as it may appear at first instance. To put it more clearly, it may take around few months to develop quality knowledge about machine learning. Moreover, if you have enrolled in a machine learning course, then the time span may be further reduced.
The feasibility of a machine learning course
If you are enthusiastic about machine learning, then investing in a machine learning course may not be a bad option. This is because the training institutions which provide such courses are equipped with state of the art facilities which give you a conducive environment to master the art of machine learning in a very short span of time. This can also be gauged from the growing popularity of these institutions over the past few years.
A plethora of applications
The applications of machine learning are too vast to summarize here but the notable need to be mentioned.
Let us take the case studies about the largest tech giants viz. Apple, Facebook and Twitter. The chatbot of Apple viz. Siri makes the use of machine learning to understand and interpret human voices and respond accordingly. Facebook makes use of machine learning in personalizing an account of an individual. Twitter uses machine learning to load user recommendations for an account.
Possibility of Problems
Pedro Domingos, in his book,” The Master Algorithm: How the quest for ultimate machine learning will remake our world” asks few brainstorming questions:
“One of the most important problems in machine learning—and life—is the exploration-exploitation dilemma. If you’ve found something that works, should you just keep doing it? Or is it better to try new things, knowing it could be a waste of time but also might lead to a better solution? Would you rather be a cowboy or a farmer? Start a company or run an existing one? Go steady or play the field? A midlife crisis is the yearning to explore after many years spent exploiting.”
Machines and humans have developed a symbiotic relationship with each other and if humans want to extract the best out of a machine, know-how about the art of machine learning is just inevitable.