Machine Learning (ML) is unique cross-functional area within Artificial Intelligence (AI) that utilizes principles from mathematics, calculus, statistics and computer science to develop algorithms that learn from data sets and experiences from real-world phenomenon and consequently, utilize that intelligence to make realistic predictions, provide prescriptions or descriptions about the phenomenon. The algorithms also adapt over time to new data and experiences to improve efficacy over time. Businesses are adopting ML in applications that include sales predictions, fault or fraud detections, machine failure predictions, image and speech recognitions, medical diagnosis, driverless cars, and so on.

ML models are typically of three types:

1. Supervised Learning: Algorithm uses training data and feedback from humans to learn the relationship of given inputs to a given output (eg, how the inputs “time of year” and “interest rates” predict housing prices)
2. Unsupervised Learning: Algorithm explores input data without being given an explicit output variable (eg, explores customer demographic data to identify patterns, detect anomalies in a streaming sensor data)
3. Reinforced Learning: Algorithm learns to perform a task simply by trying to maximize rewards it receives for its actions (eg, maximizes points it receives for increasing returns of an investment portfolio)