machine learning

Machine learning is a subfield of artificial intelligence (AI) focused on building models that improve their performance on a task by learning patterns from data rather than following handwritten rules.

Core ingredients include a representation of inputs, a loss function that scores errors, and an optimization procedure that fits parameters during training. These are followed by evaluation and deployment for inference.

Major paradigms are supervised learning for labeled prediction, unsupervised and self-supervised learning for structure and representations, and reinforcement learning for decision-making through rewards.

Practical concerns center on generalization, overfitting, regularization, data quality and splits, metrics, and monitoring models after deployment.

Split Your Dataset With scikit-learn's train_test_split()

Course

Splitting Datasets With scikit-learn and train_test_split()

In this course, you'll learn why it's important to split your dataset in supervised machine learning and how to do that with train_test_split() from scikit-learn.

intermediate data-science machine-learning numpy

By Leodanis Pozo Ramos • Updated Aug. 2, 2026
AI Coding Glossary Share Feedback