what is ai project cycle

1. Problem Scoping: This involves first analyzing the problem and determining the area or task for which AI will be used. For example, identifying student attendance at school or answering customer questions.
2. Data Acquisition: Data is crucial for AI. This step involves collecting relevant data, such as images, text, audio, video, etc.
3. Data Exploration: In this, the data is cleaned, analyzed, and formatted to make it usable for the AI ​​model.
4. Modeling: This data is then used to create an AI model, such as training a machine learning algorithm.
5. Evaluation: Finally, the model's results are tested to determine its accuracy and correctness. Improvements are also made if necessary.

In addition to these steps, every AI project cycle requires domain knowledge and the right tools. This process not only enhances personal understanding but also opens up good job opportunities in the industry.

Tip: Don't forget to explain each step of the AI ​​Project Cycle step by step in your school or college project – this will make your presentation look more engaging and understandable!