How much training data do I need?

Depends on task complexity. Simple classification: hundreds per class. Complex tasks: thousands to millions. With transfer learning, much less. Start with what you have, measure performance, add more where needed.

What if my training data is imbalanced?

Options: collect more minority class data, oversample minority/undersample majority, use class weights, synthetic data generation (SMOTE), or adjust evaluation metrics. Imbalance is common; address it explicitly.

How do I get labelled training data?

Options: manual labeling, crowdsourcing (Mechanical Turk), existing business data, weak supervision (programmatic labels), synthetic data, or active learning (model helps prioritise labeling).

Can I use public datasets for business AI?

Sometimes as a starting point or for transfer learning. But business-specific tasks usually need business-specific data. Public data helps with general capabilities; your data provides domain specificity.

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Training Data

The dataset used to train machine learning models. Training data teaches the model patterns and relationships it will apply to new, unseen data.

In-Depth Explanation

Training data is the foundation of machine learning. Models learn patterns from training examples, then generalise to make predictions on new data. Training data quality directly determines model quality.

Training data components: