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Unit 3 : Evaluating Models

SUB-UNIT LEARNING OUTCOMES SESSION/ACTIVITY/PRACTICAL

Understand the role of evaluation Session: What is evaluation?
Importance of
Model Evaluation in the development and
implementation of AI systems. Session: Need of model evaluation

Splitting the Understand Train-test split method
training set data for evaluating the performance of a Session: Train-test split
for Evaluation machine learning algorithm

Session: Accuracy
Understand Accuracy and Error
Accuracy and Error for effectively evaluating and Session: Error
improving AI models
Activity: Find the accuracy of the AI model

Session: What is Classification?

Session: Classification metrics
Learn about the different types of
Evaluation metrics evaluation techniques in AI, such as
for classification Accuracy, Precision, Recall and F1 Activity: Build the confusion matrix from scratch
Score, and their significance.
Activity: Calculate the accuracy of the classifier model

Activity: Decide the appropriate metric to evaluate the AI model

Ethical concerns
around model Understand ethical concerns Session: Bias, Transparency, Accuracy
around model evaluation
evaluation

Unit 4: Advance Python (To be assessed through Practicals)

SUB-UNIT LEARNING OUTCOMES SESSION/ACTIVITY/PRACTICAL

Understand to work with Jupyter Notebook,
creating virtual environments, installing Python Session: Jupyter Notebook
Packages.

Able to write basic Python programs using
Recap fundamental concepts such as variables, data Session: Introduction to Python
types, operators, and control structures.

Able to use Python built-in functions and
libraries. Session: Python Basics