Machine learning is a critical technique that enables AI to solve problems. Despite common misperceptions (and misnomers in popular culture), machines do not learn. They store and compute — admittedly in increasingly complex ways. Machine learning solves business problems by using statistical models to extract knowledge and patterns from data.
Machine learning is a purely analytical discipline. It applies mathematical models to data to extract knowledge and find patterns that humans would likely miss. ML also recommends actions, but it does not direct systems to take action without human intervention.
More specifically, machine learning creates an algorithm or statistical formula (referred to as a “model”) that converts a series of data points into a single result. ML algorithms “learn” through “training,” in which they identify patterns and correlations in data and use them to provide new insights and predictions without being explicitly programmed to do so. That said, machine learning is at the core of many successful AI applications, fueling its enormous traction in the market.
Deep learning (DL), a variant of machine learning algorithms, uses multiple layers to solve problems by extracting knowledge from raw data and transforming it at every level. These layers incrementally obtain higher-level features from the raw data, allowing the solution of more complex problems with higher accuracy and less manual tuning.
Organizations often treat ML and DL as the only AI disciplines and ignore other AI approaches, which unnecessarily halts or fails to start AI initiatives when ML-only solutions don’t work.
Current machine learning solutions usually need a large volume of well-labeled data, which makes this approach harder for companies with smaller datasets, poor data quality or budget constraints.
Using ML, including deep learning, to make predictions enables an AI-driven process to automate the selection of the most favorable result, which eliminates the need for a human decision maker.