Machine learning is a subset of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed. It focuses on developing computer programs that can access data and use it to learn for themselves.
The algorithm learns from labeled training data, helping predict outcomes for unforeseen data. Common applications include spam detection, image recognition, and predictive analytics.
The algorithm works with unlabeled data to find patterns and relationships. Clustering and dimensionality reduction are typical use cases.
The algorithm learns through trial and error, receiving rewards or penalties. This approach powers game-playing AI and robotics.
Training Data: The dataset used to train the model Features: Input variables used to make predictions Model: The mathematical representation learned from data Prediction: The output generated by the model
Machine learning continues to evolve rapidly, with new techniques and applications emerging constantly.