What is Machine Learning?
Machine Learning is the science of getting computers to act by feeding them data and letting them learn a few tricks on their own without being explicitly programmed.
Now, this sounds awfully a lot like a human child. So, let us consider a small scenario to understand machine learning. Now as a child if you had to distinguish between fruits such as cherries, apples, and oranges. You would not even know where to start because you are not familiar with how the fruits look.
Now as we grow up, we collect more information and start developing the capability to distinguish between various fruits. The only reason why we are able to make this distinction is that we absorb our surroundings. We gathered more data and we learn from our past experiences. It is because our brain is capable enough to think and make decisions since we have been feeding it a lot of data and this is exactly how machine learning works. It involves continuously feeding data to a machine so that it can interpret this data.
Understand the useful insides detect patterns and ident my key features to solve problems. This is very similar to how our brain works. Machine Learning is growing fastly we can not imagine the future of machine learning.
Now, let us move ahead and take a look at the different types of machine learning.
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Supervised Learning
Supervised means to oversee or direct a certain activity and make sure it is done correctly. In this type of learning the machine learns under guidance. So, at school or teachers guided us and taught us similarly in supervised learning machines learn by feeding them label data and explicitly telling them that this is the input and this is exactly how the output must look. So, the teacher, in this case, is the training data.
Unsupervised Learning
Unsupervised means to act without anyone’s supervision or without anybody’s direction. Now, here the data is not labeled. There is no guide and the machine has to figure out the data set given and it has to find hidden patterns in order to make predictions about the output. An example of unsupervised learning is an adult-like you and me. We do not need a guide to help us with our daily activities. We can figure things out on our own without any supervision.
Reinforcement Learning
Reinforcement means to establish or encourage a pattern of behavior. Let us say that you were dropped off at an isolated island. What would you do? Now initially you would panic and you would be unsure of what to do where to get food from how to live and so on. But after a while you will have to adapt you must learn how to live on the island. Adapt to the changing climates learn more to eat and what not to eat. So here you are basically following the hit and trial concept because you are new to the surroundings and the only way to learn is experience and then learn from your experience. This is what reinforcement learning is. It is a learning method wherein an agent, which is basically you stuck on the island interacts with its environment which is the island by producing actions and discovers errors or rewards. Once the agent gets trained it gets ready to predict the new data presented to it.
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