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u Tableau Public: Tableau Public is a data visualisation tool used for large datasets. It is free of cost and allows
us to share interactive data visualisations with others.
Remember, accurate and reliable data is crucial for AI systems. Data visualisation tools make exploring data
easier and fun. They help in understanding the data’s story and make it easier to spot trends and patterns. By
using these tools, you can explore, play with, and understand data, making better decisions and creating more
effective AI systems.

DATA MODELLING
Data Modelling is the stage in the AI Project Cycle in which a model for the given problem is developed using the
prepared data and appropriate technique. In the Modelling phase, training datasets are fed into the machine
and suitable algorithms are used to teach the machine.

In general, AI modelling techniques can be broadly classified into two approaches: Rule based approach and
learning based approach.

EVALUATION

Evaluation is the second last stage of the AI project life cycle. In general, Evaluation refers to the process of
understanding the reliability of a model on the basis of outputs. Once a model has been developed and trained,
it needs to go through proper testing so that one can calculate the efficiency and performance of the model.
Hence, the model is tested with the help of a sample dataset, called the Testing data, and the efficiency of the
model is estimated. The model’s efficiency is evaluated based on the following criteria:
u Precision: Precision helps us understand how good the model is at avoiding false alarms. It measures the
proportion of correctly predicted positive cases out of all the positive predictions the model made.
u Accuracy: Accuracy gives us an overall view of the model’s correctness. It calculates the percentage of
correct predictions (both true positives and true negatives) over the entire dataset.
u Recall: Recall, also known as sensitivity, tells us how well the model can correctly identify all positive
instances in the dataset.
u F1 Score: The F1 score is a special metric that combines precision and recall. It’s especially helpful when
you’re dealing with datasets where one class greatly outnumbers the other. This metric balances the trade-
off between false positives and false negatives.

DEPLOYMENT

Deployment is the process of integrating the trained model into an existing system or environment where it can
operate effectively to solve practical problems. The key stages of deployment are:
u Integration: The model is integrated into existing infrastructure, applications, or services. You can choose
from several options such as cloud-based solution or on-premises setup to deploy the model.
u Performance Monitoring: The model’s performance is monitored in real-world conditions to ensure
accuracy and address any potential performance issues.
u Scalability and Maintenance: If required, the model can be configured for increased data loads and protocols
can be established for regular updates of the model.

DOMAINS OF AI

Artificial Intelligence (AI) domains are the fields or domains in which AI can be used. The fields of AI comprise
a range of specialised fields wherein computers and algorithms with intelligence are created to solve certain
problems and applications.

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