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Questions and Answers
What is the AI Project Cycle?
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)
Which of the following are stages in the AI Project Cycle? (Select all that apply)
What is Problem Scoping?
What is Problem Scoping?
Identifying a problem and having a vision to solve it.
What does the '4Ws' in 4Ws Problem Canvas stand for?
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?
What does the 'Who' block in the 4Ws Problem Canvas identify?
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What is Data Acquisition in the AI Project Cycle?
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)
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?
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)
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?
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.
