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Stage 4: Data Modelling
An important stage in the process of AI project cycle where we decide on the technique to be followed for
building a model from the prepared data. It is a mathematical approach in which an algorithm is designed as
per the requirement of the system which is ready to be installed to analyse the data technically. In the previous
stage of data exploration, we used the graphical representation of data to make it easy to understand the trends
and patterns. But when it comes to machines, it only understands the language of 1s and 0s so they only rely on
mathematical representation of data.
Machine
Learning
Learning
Based
AI Models Deep
Learning
Rule Based
AI modelling techniques can be broadly classified into the following 2 approaches:
Rule Based Approach
This approach is based on a set of rules and facts defined by the developer and fed to the machine to perform
its task accordingly to generate the desired output. These models can operate with simple basic information
and data.
To explain it further, let's take an example. If you have a dataset that consists of weather conditions, a basis which
we can predict if the lion would be visible on a specific Safari Day to the tourists. The parameters can be cloud
cover, temperature, wind speed, humidity. When these parameters are recorded and fed in the machine giving
the favourable combinations when the Lion would be visible and rest can be considered that the lion would not
be visible. Now, to test the model, the machine is given a scenario of the cloud cover, temperature, wind speed,
humidity. The model will compare the same with the fed in the dataset and if there is a match, would let know if
the lion would be visible or not. This is called a rule-based approach.
The drawback of this approach is that the learning for the machine is static, as once trained, the machine does not
take into consideration any changes made in the original training dataset. If the machine is tested on a different
dataset from the rules and the data fed in at the training stage, the machine will fail and will not learn from the
new conditions encountered.
Learning Based Approach
This 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.
This approach is considered to take care of the challenges of rule-based systems.
For example, suppose you have a dataset of 1000 images of flowers. Now you do not have any clue as to what
trend is being followed in this dataset as you don’t know their names, colour or any other feature. Thus, you would
put this into a learning approach-based AI machine and the machine would come up with various patterns it has
observed in the features of these 1000 images.
It might cluster the data on the basis of colour , size, shape, etc. It might also come up with some very unusual
clustering algorithm which you might not have even thought of!
The Learning Based Approach can further be divided into three sections:
156 Touchpad Artificial Intelligence-X

