Machine Learning (ML)

Machine learning (ML) is a subset of artificial intelligence in which a program improves at a task by finding patterns in example data rather than following hand-written rules. The result of that learning process is a trained model that can be applied to new inputs.

Why it matters

ML powers recommendations, fraud scoring, demand forecasting, image tagging, and anomaly detection in monitoring systems. It works best when the rules are too complex to write by hand but plenty of examples exist.

Example

To predict whether a customer will cancel, you collect past customers with features like logins per month and support tickets, labeled "cancelled" or "stayed". A training algorithm adjusts the model until its predictions match those labels, then you score current customers.

features: logins=2, tickets=5, plan=basic
prediction: churn probability 0.81

Common mistakes

• Starting with ML when a simple rule or SQL query would solve the problem.
• Evaluating on the same data used for training, which hides overfitting.
• Ignoring data quality; poor labels produce poor models.

Related

• Supervised learning
• Unsupervised learning
• Deep learning
• Training data

यह पेज सामान्य जानकारी है, जिसे हमारी संपादकीय टीम ने लिखा है और दस्तावेज़ीकरण बदलने पर अपडेट किया जाता है। सर्वर और प्रदाता अलग-अलग होते हैं, इसलिए बदलाव पहले सुरक्षित तरीके से जाँचें। देखें: संपादकीय नीति और अस्वीकरण.