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Key Concepts in Machine Learning

1. Types of Machine Learning:

• Supervised Learning: The algorithm is trained on a labeled dataset, meaning that each training example is paired with an output label. Common tasks include classification and regression.
  • Example: Predicting house prices based on features like size, location, and number of bedrooms.
• Unsupervised Learning: The algorithm works on unlabeled data and tries to find hidden patterns or intrinsic structures in the input data. Common tasks include clustering and association.
  • Example: Grouping customers into different segments based on purchasing behavior.
• Semi-supervised Learning: Combines a small amount of labeled data with many unlabeled data during training. It falls between supervised and unsupervised learning.
• Reinforcement Learning: The algorithm learns by interacting with an environment, receiving rewards or penalties for actions, and aims to maximize cumulative rewards.
• Example: Training a robot to navigate a maze.