Humans can teach machines to learn and reason from past data, a concept known as machine learning. This process involves understanding preferences and making predictions based on learned data. An example provided is of a person named Paul, who decides whether he likes a song based on factors like tempo and intensity. By analyzing past choices, we can predict that if a new song has a fast tempo and soaring intensity, Paul will like it. However, if the song has a medium tempo and intensity, it's more challenging to predict his preference. This is where machine learning, specifically the k nearest neighbors algorithm, comes into play; it classifies new data points based on majority votes from existing data. The more data available, the better the prediction model will be, leading to higher accuracy. Machine learning encompasses various methods, including supervised, unsupervised, and reinforcement learning. Supervised learning is illustrated with the scenario of sorting different currencies, indicating the diverse applications of machine learning in real-life situations.