School Revise · Class 10 Artificial Intelligence · Unit 2
Code 417, Class 10. Every AI project follows clear stages. Here we learn the AI project cycle from problem to solution.
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The AI project cycle is the set of stages to build an AI solution: from understanding the problem to putting the model to use.
Problem scoping defines the problem using the 4Ws: Who has the problem, What is it, Where does it happen, and Why it matters. A good project also links to a Sustainable Development Goal (SDG).
Data acquisition collects the data needed. Data exploration studies it, often with charts, to understand and clean it before use.
Modelling trains a machine learning model on the data. Evaluation checks how well it works, and deployment puts the model to real use.
Explore the idea by tapping. The interactive opens right here in the lesson.
problem scoping.
Why.
modelling.
evaluation.
Problem scoping, data acquisition, data exploration, modelling, evaluation and deployment.
Who, What, Where and Why.
The data is studied, often with charts, and cleaned before use.
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The AI project cycle builds an AI solution in stages. Problem scoping defines the problem with the 4Ws (Who, What, Where, Why) and links to an SDG. Data acquisition collects data and data exploration studies and cleans it. Modelling trains a machine learning model, evaluation checks it, and deployment puts it to real use. |
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These free Class 10 Artificial Intelligence (Code 417) notes explain the AI project cycle, problem scoping and the 4Ws, data acquisition and exploration, modelling, evaluation and deployment with clear examples and practice, for CBSE students across India and the Gulf including the UAE, Saudi Arabia, Qatar, Oman, Kuwait and Bahrain.
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