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Machine learning is a subset of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed. Instead of writing rules for every possible scenario, we provide data and let the algorithm discover patterns on its own.
According to Stanford University's Human-Centered AI Institute, machine learning algorithms have improved in accuracy by over 50% in the last decade across major benchmarks.
Stanford HAI Annual Report 2025There are three primary approaches to machine learning, each suited for different types of problems:
The MIT Technology Review identifies supervised learning as the most commercially deployed form of ML, powering 78% of enterprise AI applications.
MIT Technology Review, 2025Every time Netflix suggests a show or Spotify builds you a playlist, machine learning is at work. These systems analyze your past behavior (what you watched, skipped, or replayed) and compare it with millions of other users to predict what you'll enjoy next. This is collaborative filtering — a supervised learning technique.
The key insight: machines don't "understand" movies or music. They understand patterns in numbers. Your preferences become data points, and the algorithm finds mathematical similarities between your patterns and others'.