AI Model Training refers to the process of feeding data into a machine learning algorithm so it can learn patterns and make accurate predictions or decisions.
This involves selecting the right model architecture, preprocessing data, adjusting hyperparameters, and evaluating accuracy. Depending on the use case, models can be trained for tasks like image recognition, natural language processing, recommendation engines, or predictive analytics.
Popular frameworks for AI training include TensorFlow, PyTorch, and Scikit-learn. Efficient training requires high-quality datasets, compute power (often GPUs), and careful tuning to avoid overfitting or bias.
