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Machine learning (ML) is a type of artificial intelligence (AI) that allows computers to learn without being explicitly programmed. This article explores the concept of machine learning, providing various definitions and discussing its applications.
Machine learning is a subfield of artificial intelligence (AI) that uses algorithms trained on data sets to create self-learning models that are capable of predicting outcomes and classifying information without human intervention.
Machine learning (ML) is a branch of artificial intelligence (AI) and computer science that focuses on the using data and algorithms to enable AI to imitate the way that humans learn, gradually improving its accuracy.
Google's fast-paced, practical introduction to machine learning, featuring a series of lessons with video lectures, interactive visualizations, and hands-on practice exercises.
concepts and techniques being explored by researchers in machine learning may illuminate certain aspects of biological learning. As regards machines, we might say, very broadly, that a machine learns
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1] .
Machine learning (ML) is a subdomain of artificial intelligence (AI) that focuses on developing systems that learn—or improve performance—based on the data they ingest. Artificial intelligence is a broad word that refers to systems or machines that resemble human intelligence.
Machine learning is a branch of artificial intelligence that enables algorithms to automatically learn from data without being explicitly programmed. Its practitioners train algorithms to identify patterns in data and to make decisions with minimal human intervention.
This course introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction. It includes formulation of learning problems and concepts of representation, over-fitting, and generalization.
This three-module course introduces machine learning and data science for everyone with a foundational understanding of machine learning models. You’ll learn about the history of machine learning, applications of machine learning, the machine learning model lifecycle, and tools for machine learning. You’ll also learn about supervised versus ...