Questions and Answers
What is the AI Project Cycle?
A step-by-step process to develop an AI project to solve a problem, consisting of five stages.
Which of the following are stages in the AI Project Cycle? (Select all that apply)
What is Problem Scoping?
Identifying a problem and having a vision to solve it.
What does the '4Ws' in 4Ws Problem Canvas stand for?
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What does the 'Who' block in the 4Ws Problem Canvas identify?
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What is Data Acquisition in the AI Project Cycle?
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Which of the following are methods for data collection? (Select all that apply)
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What is Data Exploration in the AI Project Cycle?
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What type of visual representations can be used in data exploration? (Select all that apply)
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How can data be represented for machine analysis?
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Study Notes
AI Project Cycle Overview
- A structured methodology for developing AI projects aimed at problem-solving.
- Comprised of five stages: Problem Scoping, Data Acquisition, Data Exploration, Modelling, and Evaluation.
Problem Scoping
- The initial phase involves identifying and understanding the problem to devise a solution.
- Involves using the 4Ws Problem Canvas for deeper insights.
4Ws Problem Canvas
- Who: Identifies stakeholders affected by the problem and their specific concerns.
- What: Clarifies the nature of the problem and gathers evidence of its existence.
- Where: Examines contexts and locations where the problem is prominent.
- Why: Analyzes potential benefits of the solution for stakeholders and society.
Data Acquisition
- Second stage focused on collecting necessary data for the project.
- Critical for training AI models to predict outcomes based on historical data.
Training and Testing Data
- Training Data: Historical data used to train models (e.g., previous salaries).
- Testing Data: Data used to validate the model's predictions.
Data Features
- Characterizes the specific attributes required from the data (e.g., salary amount, increment percentage).
Data Collection Methods
- Surveys, web scraping, sensors, cameras, observations, and APIs (Application Program Interfaces).
- Reliable sources of data include open government portals like data.gov.in and india.gov.in.
Data Exploration
- Involves analyzing collected data to identify patterns, trends, and relationships.
- Translates complex data into visual formats for easier understanding.
Visualization Techniques
- Using bar graphs, histograms, line charts, and pie charts to represent data visually.
Modelling
- The phase where mathematical relationships between parameters are established.
- Different AI models can be classified based on techniques used.
Rule-Based Approach
- AI modeling where specific rules defined by the developer dictate the machine's actions.
- The model operates based on predefined instructions and processes the provided data accordingly.
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Description
Explore the structured methodology of developing AI projects through five essential stages: Problem Scoping, Data Acquisition, Data Exploration, Modelling, and Evaluation. This quiz delves into the initial phase of identifying problems using the 4Ws Problem Canvas, ensuring a comprehensive understanding of the AI project lifecycle.
