Types of Machine Learning
All we need in today’s life is to automate things to much extent as possible. For that need to be done machines should work for us, To make machine work as we do, it need to be learned or trained on some data to make itself familiar with the work it has to face in future. This process of making machines learn based on patters and analytical building with less human intervention to solve problems is known as Machine Learning. There are 5 types of learning, Supervised, Unsupervised , Reinforcement, Semi Supervised , Transduction.
But let’s see about first 3 algorithms in this article.
Let’s Start with Supervised Learning
It is a part of machine learning which is a part of Artificial Intelligence. We will discuss about these parts of Artificial intelligence in our next article, Now lets focus on these types. This type of learning includes giving labelled data for machine to learn along with labels (i.e, for example if we are training a model to classify cats and dogs we feed the images of dog in a folder naming dogs and similarly for cats). So that it learns patterns of images along with the ground truth and while making predictions it can use to learned patterns to predict exact label (Either a Dog/Cat).
moving onto Unsupervised Learning
In this type of machine learning a model is trained without labelled data as the name suggests they are allowed to act without supervision. So the data given is separated as clusters based on similarity in patterns learned by machine. While predicting an image it finds the closest cluster to with the image patterns match and outputs that class. This type of model helps in grouping the similar data from a large set of data. Below figure illustrates about unsupervised learning.
Last but not least, Here comes Reinforcement Learning
This type of learning includes agents, environment. Where agent performs actions in an environment in order to maximize the reward. If the action taken results positive outcome it is rewarded or it is penalized. So this model learns based on the interpreter outcome by reducing the penalties and increasing rewards. This process is repeated until it reaches a correct path with best accuracy for prediction.
So, This is about types of machine learning. Mostly used is supervised machine learning as it helps machine to learn fast and to make accurate prediction. But it is used to make predictions based on trained data only.
in Unsupervised learning model is able to find the cluster for a image which is not trained on based on similarity between trained and test image. Coming to Reinforcement it is similar to human child as they learn based on outcomes of performing certain things but it takes much more time to learn than other techniques.
Thank You!!! for reading my Article. Let’s meet with a new one Stay Tuned…..
