What is machine learning in simple terms?

It is a method for estimating patterns from examples so software can predict, rank, classify or detect new cases under a defined task.

What is the difference between supervised and unsupervised learning?

Supervised learning uses labels for a target outcome; unsupervised learning explores structure such as clusters or anomalies without that target.

Why do beginners need a baseline?

A baseline shows whether machine learning and added complexity improve meaningfully over a simple rule or historical method.

Overfitting occurs when a model learns training-sample quirks and performs worse on new representative data.

Why can accuracy be misleading?

A model can achieve high accuracy by predicting a common class while failing to find rare, costly events. Metrics must reflect consequences.

What is data leakage?

Leakage is using information during training or evaluation that would not legitimately be available when the real prediction is made.

Which algorithm should a beginner learn first?

Begin with linear or tree-based models and a reproducible evaluation pipeline. Understanding data and errors matters more than starting with a complex neural network.

Machine Learning

Machine Learning Basics: Concepts and Evaluation

Machine learning uses examples and feedback to construct a model that makes predictions or supports decisions. The algorithm is only one component: the task definition, data, evaluation and operating environment determine whether a system is useful. This

Ruskin Felix Barar19 December 2025Updated 6 October 202616 min read

Machine Learning Basics: Concepts, Data and Evaluation

Machine learning uses examples and feedback to construct a model that makes predictions or supports decisions. The algorithm is only one component: the