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Questions and Answers
What is the primary goal of data acquisition in an AI project?
What is the purpose of visualizing the collected data?
What is the next step after selecting a model?
What is the purpose of testing the model on newly fetched data?
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What is the final step in the AI project cycle?
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What is the primary reason for researching online for various models?
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Study Notes
Data Acquisition
- Data acquisition is the first step in building an AI project, and it involves collecting data from various reliable and authentic sources.
- The collected data is typically in large quantities, making it essential to visualize it using different representations like graphs, databases, flow charts, and maps.
Pattern Exploration
- Visualizing data helps in identifying patterns, which is crucial in deciding the type of model to build for the project.
- Exploring patterns enables the selection of suitable models that can achieve the desired output.
Model Selection
- Researching online helps in selecting various models that can provide a suitable output.
- The selected models are tested to determine the most efficient one.
Model Development
- The most efficient model is used as the base for the AI project, and the algorithm is developed around it.
- The developed model is then tested on newly fetched data to evaluate its performance.
Model Evaluation
- The test results are used to evaluate the model and identify areas for improvement.
- The evaluation process is crucial in refining the model and ensuring its accuracy.
Project Completion
- The project cycle is complete after the model evaluation, and the final output is a fully developed AI project.
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