Podcast
Questions and Answers
Problem scoping involves defining the project’s goal and analyzing the different aspects affecting the problem.
Problem scoping involves defining the project’s goal and analyzing the different aspects affecting the problem.
True
Which of the following is NOT one of the 4 W's in Problem Scoping?
Which of the following is NOT one of the 4 W's in Problem Scoping?
What is the process of collecting correct and dependable data called?
What is the process of collecting correct and dependable data called?
Data acquisition
The 4 W’s of Problem Scoping are Who, What, Where, and _____ .
The 4 W’s of Problem Scoping are Who, What, Where, and _____ .
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Match the following elements of Problem Scoping with their descriptions:
Match the following elements of Problem Scoping with their descriptions:
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What is structured data characterized by?
What is structured data characterized by?
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Unstructured data can be easily stored and managed due to its traditional data models.
Unstructured data can be easily stored and managed due to its traditional data models.
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What percentage of the dataset is typically used for training in machine learning?
What percentage of the dataset is typically used for training in machine learning?
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An API is a software interface that enables two _____ to communicate with one another.
An API is a software interface that enables two _____ to communicate with one another.
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Match the following data collection methods with their descriptions:
Match the following data collection methods with their descriptions:
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Study Notes
AI Project Cycle
- The AI Project Cycle is a step-by-step process to derive value from an AI project and solve problems.
- The cycle has five stages: Problem Scoping, Data Acquisition, Data Preprocessing, Model Training, and Model Evaluation and Deployment.
Problem Scoping
- Problem scoping is defining the project's goal and identifying the problem's different aspects.
- The '4Ws' help identify and understand the problem: Who, What, Where, and Why.
- Who identifies the stakeholders directly and indirectly affected by the problem.
- What analyzes the problem's nature, gathering evidence to confirm its existence.
- Where identifies the situation and where the problem arises.
- Why explains the need to address the problem and the advantages for stakeholders after solving it.
- The problem statement template summarizes the key points gathered from the '4Ws'.
Data Acquisition
- Data acquisition involves collecting accurate and reliable data for analysis.
- Data can be structured, unstructured, or both.
- Structured data is organized in a standardized format, easily accessible by humans and programs. It includes numbers, characters, and special characters.
- Unstructured data doesn't follow traditional data models, making it difficult to store and manage. Examples include videos, audio, images, and log files.
- Dataset is a collection of data in tabular format related to a specific subject.
- Datasets are divided into training and test datasets.
- Training dataset (usually 80%) teaches a machine learning model to make judgments or perform tasks.
- Test dataset (usually 20%) is used to evaluate the model's performance on unseen data.
Acquiring Data from Reliable Sources
- Surveys gather data through questionnaires from a predetermined sample to gain insights.
- Cameras collect visual data (unstructured) that can be analyzed by machine learning.
- Web Scripting collects structured data from the internet, such as market research or price tracking.
- Observation provides data through attentive monitoring and noticing patterns.
- Sensors collect data about physical properties using devices like biometrics.
- Application Program Interface (API) allows two applications to communicate and exchange data.
System Map
- A System Map visually represents the relationships between components within a system.
- The map depicts cause and effect relationships using arrows to connect elements.
- An example is the Water Cycle System Map, where all constituents are circled, and arrows show their interactions.
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Description
This quiz covers the five essential stages of the AI Project Cycle: Problem Scoping, Data Acquisition, Data Preprocessing, Model Training, and Model Evaluation and Deployment. Participants will learn how to effectively navigate each phase to derive value from AI initiatives. Assess your understanding of the fundamental aspects involved in each stage of the project cycle.
