Machine learning takes place when a computer uses an artificial intelligence (AI) algorithm to learn on its own through the analysis of large amounts of data and then creates a model for future predictions. More generally, machine learning simulates human behavior because the machine learns by itself, not through human intervention.
Many, but not all, computer scientists consider machine learning to be a branch or subset of artificial intelligence.
Which processes can machine learning perform?
Machine learning is used in a wide variety of business, healthcare, and fraud detection applications as well as in devices and processes that are useful in everyday life. These include:
- Virtual personal assistants, such as Alexa and Siri
- Tailored recommendations, such as the "suggested for you" suggestions on Amazon
- Voice recognition
- Computer vision
- Bank and credit card fraud detection
- Internet search engines
- Wearable medical devices
- Computer games
- Email filtering for spam and malware
- Data analysis
Machine learning is also used in very complex products, such as self-driving cars, and complicated processes, including the development of new drugs and vaccines. RELATED: The healthcare digital transformation
Is all machine learning the same?
While machine learning's end goal—to simulate human thinking processes—is part of every machine learning method, computer scientists have developed three main approaches. In supervised learning, the computer algorithm analyzes training data that has been labeled to help the machine identify different types of outcomes.
The algorithm then makes predictions about future outcomes. If those predictions are incorrect, the algorithm can change its model in order to improve future predictions.
Unsupervised learning uses training data that is not labeled. The computer algorithm analyses the unlabeled training data in order to look for patterns and structures.
Reinforcement learning takes a different approach. The machine learning algorithm explores its environment and uses trial and error to determine which behaviors result in rewards. It then adjusts its model in order to obtain more rewards and fewer punishments for its choices.
This process is sometimes described as finding a balance between exploration and exploitation.
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