CBSE Class 10 AI – Revisiting AI Project Cycle Notes
1. What is AI project cycle?
Ai project cycle is defined as a sequence of steps that are required to develop an AI application. These steps clearly define how to start the AI project till it is completed.
2. List the names of steps wired in AI project cycle.
There are five steps in AI project cycle
i. Problem Scoping
ii. Data Acquisition
iii. Data Exploration
iv. Modelling
v. evaluation
3. What is the significance of AI project cycle?
AI Project cycle is very important because without AI Project cycle, it is impossible to develop an AI application.
4. What do you mean by Problem Scoping?
Problem Scoping is defined as the stage in which aim and scope of the AI project is decided.
5. What activities are carried out during problem scoping phase?
i. Defining objectives of the project
ii. Defining expected results to be produced by project.
iii. Knowing expectations of stakeholders
iv. Important resources and steps
v. Success metrics
6. What do you mean by Data acquisition?
Data acquisition is the process to gather data according to requirements defined in problem scoping phase.
7. What activities are carried out during Data acquisition phase?
i. Standard form of data is extracted from dissimilar sources of data.
ii. Data is produced in the most appropriate form.
8. What do you mean by data exploration?
Data Exploration is the process to clean and normalize the collected data.
9. What activities are performed during data exploration phase?
i. Data from multiple sources is converted into a format suitable for ai project.
ii. Clean and normalise the data.
iii. Choose the model that can solve the problem.
10. What do you mean by modelling phase?
It is the stage in which AI models are created from data.
11. What activities are performed during modelling phase?
i. Ai models from data are created and best model among them is chosen.
ii. After choosing best model, AI algorithms are developed.
iii. Relationship among data is also defined.
12. What do you mean by evaluation?
Evaluation is the process to test the accuracy and performance of an AI model.
13. What activities are performed during evaluation phase?
i. Accuracy of the AI project is tested against the actual data.
ii. Results are evaluated to see whether model needs any improvement.
iii. It is checked whether model can be deployed or not.
14. What do you mean by training dataset?
It is the set of data used to train an AI model.
15. What do you mean by testing dataset?
It is the set of data used to test the AI model to evaluate its performance after its training is complete.