Machine learning algorithms can be trained in a variety of ways, each with advantages and disadvantages. Machine learning is broadly classified into four groups based on these methodologies and modes of learning:
This type of ML is supervised, in which machines are taught on labelled datasets and then allowed to predict outputs based on the training. Certain input and output parameters are already mapped, according to the labelled dataset. As a result, the machine is taught using the input and output. In later rounds, a device is created to predict the outcome using the test dataset.
Consider the following input dataset: parrot and crow photos. Initially, the system is trained to recognize the images, which include the color, eyes, shape, and size of the parrot and crow. Following training, an image of a parrot is presented as input, and the machine is expected to identify the object and forecast the output. To create a final prediction, the trained computer examines the input image for numerous aspects of the object, such as colour, eyes, shape, and so on. In supervised machine learning, this is the process of identifying objects.
The supervised learning technique’s primary goal is to translate the input variable (a) to the output variable (b). There are two broad categories of supervised machine learning: