Your Conversational AI Glossary
Machine Learning (ML)
What is a Machine Learning?
Machine learning (ML) is a branch of artificial intelligence that enables computers to learn from data and improve their performance over time — without being explicitly programmed. It’s used to identify patterns, make predictions, and automate decision-making in real-world applications.
How Does Machine Learning Work?
Machine learning models are trained using large datasets. They analyze input data, identify trends or relationships, and adjust their internal logic to improve accuracy. The process involves:
Training – Feeding data into a model to learn patterns
Validation – Testing the model’s performance on unseen data
Prediction – Using the model to make informed guesses or classifications
There are three main types of ML:
Supervised Learning – Trained on labeled data (e.g., predicting prices)
Unsupervised Learning – Finds patterns in unlabeled data (e.g., customer segmentation)
Reinforcement Learning – Learns through trial and error to achieve a goal (e.g., robotics)
Why Is Machine Learning Important?
Machine learning powers many modern tools and services. It helps businesses:
Predict customer behavior
Detect fraud or anomalies
Automate repetitive tasks
Personalize experiences
Power AI assistants and chatbots
It’s the foundation for many advanced AI applications — including voice recognition, recommendation engines, and virtual receptionists.
Examples of Machine Learning in Action
An AI receptionist learns to better understand caller intent over time
E-commerce sites recommend products based on your shopping behavior
Banks use ML to detect unusual transactions and potential fraud
Email platforms filter spam using pattern recognition
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