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Data Modelling Techniques
In AI modelling, we will be developing different algorithms which we call models, and these models can be
trained to get intelligent output. In other words, we can say we write codes to make a machine artificially
intelligent.
AI modelling techniques can be broadly classified into two approaches. 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 relationship 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
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 the model where the relationship or patterns in the data are not defined by
the developer. Random data is fed into the machine and the machine develops its own pattern or trends based
on data outputs. It is an alternative method to address some of the challenges of rule-based systems.
AI Project Cycle 225

