How do I prepare datasets for AI?

How do I prepare datasets for AI?

Preparing datasets for AI involves several steps: data collection, cleaning, labeling, and splitting.

What types of data should I collect?

You should collect data relevant to your AI task, such as images, text, or numerical data, depending on your model.

How do I clean the data?

Data cleaning includes removing duplicates, handling missing values, and correcting inconsistencies in the dataset.

What is data labeling and why is it important?

Data labeling is the process of annotating data with meaningful tags, which is crucial for supervised learning models.

How do I split the dataset for training and testing?

Typically, you split the dataset into training, validation, and test sets, often using a ratio like 70:20:10.