Types of ML

ML encompasses a multitude of algorithms and topics. While every such algorithm that makes up an ML model is nothing but a mathematical computation on given data, the form of data that is provided and the manner of the task to be performed on it might hugely vary. Sometimes, you might want your ML model to predict future house prices based on the data of previous house prices with respect to details of the house such as the number of rooms and number of stories it has, and at other times, you might want your ML model to learn how to play computer games against you. You can easily expect the input data for the first task to be in tabular format, but for the second example, you might not be able to come up with the same. Hence, ML algorithms branch into three major categories and another form that derives from them, based on the input data they receive and the kind of output they are supposed to produce, namely, the following:

• Supervised learning
• Unsupervised learning
• Reinforcement learning
• Semi-supervised learning

The following diagram captures the three major types of ML, along with the hybrid form as a fourth type, and a very brief summary on each type:

You may have heard of the fourth form of ML—semi-supervised learning, which fuses both the worlds of supervised and unsupervised learning.

Let's now understand these types of ML in greater depth, according to how they function and the types of problems they can be used to solve.