Function, Types of Machine Learning and Examples
Machine learning can be regarded as one of the most active areas of Artificial Intelligence. We now have machines that learn to drive cars, search for new pharmaceuticals and even be expert players in games that require complex strategies and creativity.
Machines are learning how to make judgments through pattern matching and strategic decision-making.
A simple definition of Machine Learning (ML)
Learning would be the ability of algorithms to learn from data in such a way that they can improve their function in the future. It is a subset of Artificial Intelligence that provides machines the ability to learn automatically and improve their experience without being explicitly programmed.
Types of Machine Learning
Supervised Learning In supervised learning, machines learn by feeding them explicit data and explicit elucidation of what the input is and how the output should look like. The word supervise by itself means to oversee or direct activity and make sure it is done correctly. And here the machines learn under guidance with a lot of data that is fed into them.
For example,
imagine being in a classroom where in we have a teacher to guide us with everything.
The teacher imparts knowledge to us and we learn from what is being taught.
That’s exactly how supervised learning functions with machines too.
Unsupervised learning
In unsupervised learning, the machines are expected to function without the help of an explicit guide. The machine has to figure out the data sets given and it has to find hidden parts in order to make predictions. Here the data provided to the machine isn’t labeled and machine has to learn without any supervision.
For example,
we are abroad in a fruit stall and unable to identify the name of a particular fruit.
We can know its name and taste by enquiring in the nearby stalls for reference.
Hence we gain knowledge without an explicit supervisor.
Reinforcement
Learning In reinforcement learning, the machines learn through experience. Here, the machine is put in an unknown environment and allowed to interact with its environment by producing actions and it discovers errors or rewards. This method helps to learn how to attain complex objective or maximize a specific dimension over many steps.
For example,
though we are a novice in a game, we tend to understand the functioning of the game through the steps that are being followed.
Similarly, in reinforcement learning, machines learn from the associations between stimuli, actions, and the occurrence of pleasant events, called rewards, or unpleasant events called punishments.
While many have a misconception that artificial intelligence, machine learning and deep learning are all the same, it is not true.
They all are inter-connected technologies.
With such exponential growth in AI, machine learning has become the most trending field in the 21st century. It has started to redefine the way we live and that is why it becomes essential to learn more about it. Perhaps, the ability to learn and function by experiences makes Machine learning the most exciting technologies one could have ever come across.
