Machine learning is a vast field comprising of various data related operations such as analysis, prediction, decision making and much more. These applications require a set of well-defined steps to proceed with the idea designed for model construction. A set of well-defined instructions that produces some output or accomplishes a particular task is called an algorithm. The machine learning algorithms are broadly classified into 3 categories – Supervised, Unsupervised and Reinforcement Learning.
To choose an appropriate algorithm in machine learning, identifying the kind of problem is very necessary as each of these algorithms obeys a different plan of attack to deal with the proposed problem. Supervised learning uses an approach where the output is already known to the user or the individual while unsupervised learning concentrates on the concept of similarity in properties of the objects. Reinforcement learning differs from both of them and uses the art of learning from experiences.
Supervised learning is used in machine learning tasks such as classification, regression, and analysis. It is considered as a concept that deals with labeled values. This means that the objects are categorized or assigned to different classes based on their properties. The algorithm implementation in supervised learning is done by a two-step procedure namely model construction and model utilization.
Firstly, the given data is cleaned and divided into training and testing sets. The model gains the ability to produce output by learning from the instances contained in the training set. The test set gives a measure of the model performance by producing accuracy. The accuracy indicates the amount or rather the percentage of unseen data that was computed correctly by the applied algorithm.
There are several metrics to determine the performance of the model and improve it if the performance is not up to the mark. This includes performing tasks like cross-validation, parameter tuning, etc. Hence, we can conclude that supervised learning uses labeled classes and target values to classify an unseen data point.
In contrast to the supervised approach that already knows the predicted outcome, unsupervised learning uses the basis of similarity in properties to classify the unseen data points in the given n-dimensional space.
The main idea is to take a data point that is new to the given space, extract the behaviors of the data point, compare it with the already existing properties of the other objects and accordingly classify or categorize them into the appropriate group. The common examples of unsupervised learning are clustering, Apriori and K-means algorithm.
Reinforcement learning is very similar to the animal kingdom where the animals do not train their offspring to perform a particular task but they leave them out in the ecosystem to learn from the experiences that it gains while struggling to accomplish a particular task.
The basic idea of performing reinforcement learning is to let the model learn on its own. It uses a trial and error strategy to gain knowledge from the available environment. According to the experiences gained from the conditions, it is exposed to, appropriate predictions and decisions are made. Markov Decision Process is an example of reinforcement learning.
Because of the wide variety of applications offered by machine learning, there are several Machine learning courses dedicated to offering the training in machine learning algorithms so that an individual can recognize the problem efficiently and work towards building an appropriate solution. Learning and understanding of machine learning algorithms are very easy. It just needs a proper classification of the interest in performing the desired operation.