The following three definitions that follow are taken from, ‘Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems‘, by Aurélien Géron. Found here: https://shorturl.at/haw5k
“Machine Learning is the science (and art) of programming computers so they can learn from data.”
“[Machine Learning is the] field of study that gives computers the ability to learn without being explicitly programmed.”
“A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.”
It seems that the common theme amongst all three definitions is the ability of computers to be able to learn. I believe it is crucial to be able to keep these definitions in your head and be able to reproduce them when needed.
One of the examples of machine learning that the book gives and goes into detail about is the spam filter for emails. The author explains that there is a segment of emails that users can flag as spam and another section being ‘ham’ or normal, non-spam emails. The collection of emails that is used in the learning process is called the training set. With each example within that set being the training instance.
The author uses the 3rd definition of machine learning to apply the example of the spam filter:
“the task T is to flag spam for new emails, the experience E is the training data, and the performance measure P needs to be defined; for example, you can use the ratio of correctly classified emails”
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