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  • Machine learning - Wikipedia
    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 generalise to unseen data, and thus perform tasks without explicit instructions [1]
  • What Is Machine Learning? Definition, Types, and Examples
    Machine learning is a subfield of artificial intelligence (AI) that uses algorithms trained on data sets to create self-learning models capable of predicting outcomes and classifying information without human intervention
  • What Is Machine Learning (ML)? | IBM
    What is machine learning? Machine learning (ML) is a branch of artificial intelligence (AI) focused on enabling computers and machines to imitate the way that humans learn, to perform tasks autonomously, and to improve their performance and accuracy through experience and exposure to more data
  • Machine learning, explained - MIT Sloan
    Machine learning takes the approach of letting computers learn to program themselves through experience Machine learning starts with data — numbers, photos, or text, like bank transactions, pictures of people or even bakery items, repair records, time series data from sensors, or sales reports
  • What is Machine Learning? Definition, Types, Tools More
    Machine Learning, often abbreviated as ML, is a subset of artificial intelligence (AI) that focuses on the development of computer algorithms that improve automatically through experience and by the use of data
  • What is Machine Learning? - GeeksforGeeks
    Machine learning is a branch of artificial intelligence that enables algorithms to uncover hidden patterns within datasets It allows them to predict new, similar data without explicit programming for each task
  • Introduction to Machine Learning - MIT OpenCourseWare
    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




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