In the context of Artificial Intelligence (AI) and Machine Learning (ML), training refers to the process of teaching a model to recognize patterns and make decisions by exposing it to a large dataset. During training, the model adjusts its internal parameters to minimize errors in its predictions or classifications.
Key Aspects of Training in AI and ML:
- Data Preparation: The first step involves gathering and preprocessing data to ensure it’s suitable for training. This may include cleaning the data, handling missing values, and normalizing or scaling features.
- Model Selection: Choosing an appropriate algorithm or model architecture is crucial. The choice depends on the specific task, such as classification, regression, or clustering.
- Training Process: The model learns by processing the training data, adjusting its parameters to reduce the difference between its predictions and the actual outcomes. This is typically achieved through optimization techniques like gradient descent.
- Evaluation: After training, the model’s performance is assessed using a separate validation or test dataset to ensure it generalizes well to new, unseen data.