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Data Modelling Techniques
In AI modelling, we develop different algorithms called models, which can be trained to produce intelligent output.
In other words, we write code to make a machine artificially intelligent.
AI modelling techniques can be broadly classified into two approaches, namely, rule-based and learning-based.
Let us learn about them in detail.
Rule-Based Approach
Rule-based approach is based on a set of rules and set of facts already fed to the machine to generate the desired
output. These models can operate with simple basic information and data. The relationships or patterns in the data
is defined by the developer.
To explain it further, let's take an example. You have a dataset comprising 100 images of cars and 100 images of
cycles. To train your machine, you feed this data and label each image as either a car or a cycle. Now if you test
the machine with an image of a car, it will compare with the trained data and according to the labels of the trained
data it will identify it as a car. This is called a rule-based approach. The rules given to the machine in this example
are the labels assigned to the training data.
Rule-based Approach
Labelled Datasets
Used to Train Machine Model
Output Used for Testing Training Data
Machine Identifies the Image as Car Testing Data
Learning-Based Approach
Learning-based approach refers to a model where the relationships or patterns in the data are not explicitly
defined by the developer. In this approach, random data is fed into the machine and the machine develops its
pattern or trends based on the data outputs. It is an alternative method to address some of the challenges of rule-
based systems.
This approach is typically followed when the dataset fed to the machine is unlabelled and too random. The machine
tries to extract similar features and clusters them in the same datasets. In the end, the machine tells the trends
which are observed in the training data.
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