Imagine you want a computer to be able to recognize a friend's face in a photo. Machine learning would be used to train the computer to identify the features and characteristics of your friend's face so that it can recognize them in other photos.
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This is done by feeding the computer a large number of images labeled as “friend” or “non-friend” so that it can learn to distinguish the differences between the two categories.
Machine learning has many practical applications, from speech recognition systems and fraud detection in financial transactions to self-driving cars and medical diagnostics. In short, machine learning allows machines to “learn” from data and improve the accuracy of their decisions over time.
Useful sources for this text include the book “Machine Learning: A Probabilistic Perspective” by Kevin P. Murphy, the Machine Learning Mastery website maintained by Jason Brownlee, and specialized publications such as the Journal of Machine Learning Research and the IEEE Transactions on Neural Networks and Learning Systems.
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