In the AI project life cycle, the second stage is Data Acquisition, dedicated to gathering essential information for the project. Data, defined as a compilation of facts or information gathered for analysis or reference, plays a pivotal role in training an AI system to make accurate predictions.
For instance,imagine developing an AI application that predicts stock prices based on historical market data. To train the AI model, you would input a dataset containing past stock prices and related information. This dataset, utilized for instructing the machine, is commonly referred to as training data. Once the model is adequately trained, it can then forecast future stock prices. The dataset used for validating the accuracy of these predictions is known as testing data.
