AI Project Workflow

The video outlines the workflow of AI projects, starting with data collection and cleaning, followed by training machine learning models. It emphasizes the importance of testing models with new data and tuning hyperparameters to improve performance. Once the model is accurate, it can be deployed. The video concludes with a preview of upcoming classes.

What are the primary tasks that machine learning models can perform?

• Web development, app development, and software testing

• Data encryption, decryption, and storage

• Classification, regression, and clustering

• Image rendering, video editing, and sound mixing

Why is data collection considered a crucial step in AI projects?

• It is the least time-consuming step

• It ensures the data is encrypted

• It provides the raw material for training models

• It guarantees immediate deployment of the model

What is the purpose of data cleaning in AI projects?

• To encrypt the data for security

• To enhance the speed of data processing

• To remove missing values and duplicates

• To visualize the data in graphs

What is the role of hyperparameters in machine learning models?

• They determine the data collection method

• They are used to encrypt the model

• They adjust the algorithm's behavior for better results

• They visualize the model's performance

What should be done if a trained model does not perform well on new data?

• Deploy the model immediately

• Collect more data and test again

• Tune the hyperparameters and retrain

• Ignore the performance issues