Machine learning concepts revolve around the idea of enabling computers to learn from data and make predictions or decisions without explicit programming. The input in machine learning refers to the data fed into algorithms for analysis. This data, often called features, consists of various attributes that describe the characteristics of the problem being solved. For instance, in a housing price prediction model, features might include the size of the house, number of rooms, and location. Understanding how to effectively select and preprocess these inputs is crucial for building accurate and efficient machine learning models.