The use of artificial intelligence in machines is termed Machine learning. Before going towards machine learning, let’s have a look at what is artificial intelligence?
Artificial intelligence is the art and engineering of developing and modifying intelligent machines and programs. Machine learning comes under the applications of Artificial intelligence. The main aim of machine learning revolves around the development of computer programs that can reach the data and use the data to draw conclusions and learn from them. It gives systems the capability to learn automatically and to improve and modify themselves from their experiences without being explicitly programmed.
Four categories appear under machine learning:
As the name suggests, the principal purpose of supervised learning is to acquire a model that can predict the future precisely based on past structured data. Two steps are involved in this module of learning i.e train and predict. In this type of learning, the system will iterate until it finds the best model and then integrates trained models into applications.
For Example: Predicts whether an email is spam or not by just having the input of past data.
Supervised machine learning categorize further into two domains and those are:
The outcome is categorical or certain (Ex. Gender)
The outcome is a continuous value (Ex. Age)
The primary goal of unsupervised learning is to group unstructured data based on similarities. It can automatically find relationships, correlations, and similarities within the provided data. Unsupervised learning is also used as a pre-processing step for supervised learning.
Same as Supervised learning, Unsupervised learning is also classified into sub-domains:
It groups similar data points together. Clustering coins based on similarity could be through the size, the material, or the language. Clustering can work with Categories and Numbers.
ii. Dimensionality Reduction
It combines the features that are comparable or do the same thing into a smaller set by plucking features that capture most of the variance in the data, leading to a set of new features that’s smaller in size.
As the name suggests, Semi-supervised learning is a blend of Supervised and Unsupervised Learning. Structure and Unstructured data are used. Automatically finds relationships and similarities within data that is unstructured. It is used in correlation with Structured data to help Unstructured data become more accurate.
Reinforcement learning is a type of machine learning that is directly used with Artificial Intelligence. It is more commonly used in robots and products that simulate human behavior. It is accomplished through trial and error (similar to how humans gain knowledge and wisdom). This is what is the basis behind what is considered Strong Artificial Intelligence (a robot that is just like a human). This is also the basis behind video games and Virtual Reality.
If we compare Supervised and Unsupervised machine learning, we came across the following differences:
i. In supervised learning, there is a knowledge of output but in unsupervised learning, there is zero knowledge regarding output.
ii. Data is labeled with a class or value but in unsupervised learning the data is unlabelled or the values are unknown.
iii. The goal of Supervised learning is to predict class or value labels and the goal of unsupervised learning is to determine data patterns or groupings.
iv. Supervised learning needs to be dealt with more algorithms but unsupervised learning needs to be dealt with fewer algorithms.