Machine Learning is the science of designing systems that can learn and improve from experience. In other words, ML algorithms are capable of automatically identifying patterns within data, making predictions, and adapting over time, without requiring direct human intervention.

Key components of ML include:

• Supervised Learning: Training a model on labeled data (i.e., data with known outcomes) to make predictions or classify new, unseen data.

• Unsupervised Learning: Identifying hidden patterns or structures in unlabeled data. This is commonly used in clustering, anomaly detection, and association tasks.

• Reinforcement Learning: A technique where an agent learns by interacting with an environment and receiving feedback in the form of rewards or penalties, typically used in robotics and gaming.

• Deep Learning: A more advanced subset of ML that uses neural networks with many layers (hence “deep”) to analyze complex data such as images, text, and speech.