Podcast
Questions and Answers
What is the primary goal of the Problem Scoping step in the AI Project Cycle?
What is the primary goal of the Problem Scoping step in the AI Project Cycle?
Which of the following blocks is NOT included in the 4Ws Problem Canvas?
Which of the following blocks is NOT included in the 4Ws Problem Canvas?
What is the purpose of the 'Why?' block in the 4Ws Problem Canvas?
What is the purpose of the 'Why?' block in the 4Ws Problem Canvas?
Which Sustainable Development Goal focuses on economic equality?
Which Sustainable Development Goal focuses on economic equality?
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During the Problem Scoping stage, what should be done if no problems are initially observed?
During the Problem Scoping stage, what should be done if no problems are initially observed?
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What type of data is specifically defined as data that can only take certain values?
What type of data is specifically defined as data that can only take certain values?
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Which data type is characterized by the absence of a predefined structure or organized format?
Which data type is characterized by the absence of a predefined structure or organized format?
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What is the main purpose of training data in an AI project?
What is the main purpose of training data in an AI project?
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Which of the following is a common method of data acquisition?
Which of the following is a common method of data acquisition?
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Which of the following data features would NOT be relevant when predicting an employee's salary?
Which of the following data features would NOT be relevant when predicting an employee's salary?
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Study Notes
AI Project Cycle
- AI Project Cycle consists of a series of steps taken when executing an AI project.
- Problem Scoping is the initial step where the project's goals are outlined by identifying the problem to be solved.
- If noticeable problems are absent, reference the United Nations’ 17 Sustainable Development Goals for potential issues to address.
United Nations Sustainable Development Goals
- No Poverty
- Zero Hunger
- Good Health and Wellbeing
- Quality Education
- Gender Equality
- Clean Water and Sanitation
- Affordable and Clean Energy
- Climate Action
- Life on Land
- Life Below Water
- Responsible Consumption and Production
- Decent Work and Economic Growth
- Industry, Innovation and Infrastructure
- Sustainable Cities and Communities
- Peace, Justice, and Strong Institutions
- Reduced Inequalities
4Ws Problem Canvas
- Framework for dissecting the problem:
- Who? Identifies stakeholders affected by the problem and outlines the beneficiaries of the solution.
- What? Defines the problem’s nature and gathers evidence that it exists, drawing from various sources such as media.
- Where? Examines the context, location, and environments where the problem is most prominent.
- Why? Explores the benefits of solving the problem for the stakeholders and society as a whole.
Problem Statement Template
- Condenses findings from the 4Ws Problem Canvas into a comprehensive synopsis to clarify the problem and proposed solutions.
Data Acquisition
- Involves gathering data needed for the project, essential for training AI models to make predictions.
- Data is categorized into:
- Training Data: Historical data used to train the model (e.g., past salaries to predict future salaries).
- Testing Data: Data set used to test the model's predictions.
- Importance of acquiring authentic, relevant data corresponding to the scoped problem statement.
Data Features Collection Methods
- Various methods for data collection include:
- Surveys
- Web Scraping
- Sensors and Cameras
- Observations
- APIs (Application Programming Interfaces)
- Emphasis on using reliable sources and compliance with data privacy regulations.
Classification of Data
- Data can be classified into three main types:
-
Basic Data:
- Text
- Numeric
- Discrete Data: Limited values that can be counted, such as the number of students.
- Continuous Data: Measured data that can take on an infinite range of values, for example, height or weight.
-
Basic Data:
Data Structure Types
- Structured Data: Data with a defined format, typically organized in tables (e.g., mark sheets or schedule tables).
- Unstructured Data: Lacks a predefined format, comprising a large portion of existing data (e.g., videos and photos on social media).
- Semi-Structured Data: Combines elements of structured and unstructured data, retaining some organizational properties while lacking full structure.
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Description
Explore the AI Project Cycle as outlined in your Class X curriculum for the 2024-2025 session. Understand the process starting from problem scoping to achieving your AI project goals. This quiz will guide you through each step of the cycle, ensuring clarity and comprehension.
