Machine Learning is a branch of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed. It focuses on developing algorithms and models that can analyze data, make predictions, and learn patterns from examples.
- Glossary > Letter: M
What does "Machine Learning" mean?

Use Cases
Recommendation Systems:
Providing personalized recommendations based on user behavior.
Speech Recognition:
Converting spoken language into text for applications like virtual assistants.
Predictive Analytics:
Forecasting future trends and behaviors based on historical data.

Importance
Automation:
Automates decision-making and predictive tasks that are difficult or impractical for humans.
Scalability:
Scales well with large datasets and complex problems, providing insights and solutions.
Innovation:
Drives advancements in various fields by discovering patterns and making data-driven predictions.

Analogies
Machine Learning is like teaching a child to ride a bike. Initially, you provide guidance and corrections based on their experience (data). Over time, the child learns to balance and steer independently, adapting to new situations based on previous learning.
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