What You Need To Know About Machine Learning
Title : What You Need To Know About Machine Learning
Edition :
Year : 2016
Authors : Gabriel Cánepa
Publisher : Packt
Preface
It is a well-established fact that we, as human beings, learn through experience. During our early childhood, we learn to imitate sounds, form words, group them into phrases, and finally how to talk to another person. Later, in elementary school, we are taught numbers and letters, how to recognize them, and how to use them to make calculations and spell words. As we grow up, we incorporate these lessons into a wide variety of real-life situations and circumstances. We also learn from our mistakes and successes, and then use them to create strategies for decision making that will result in better performance in our daily lives. Similarly, if a machine--or more accurately, a computer program--can improve how it performs a certain task based on past experience, then you can say that it has learned or that it has extracted knowledge from data.
The term machine learning was first defined by Arthur Samuel in 1959 as follows:
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Preface
It is a well-established fact that we, as human beings, learn through experience. During our early childhood, we learn to imitate sounds, form words, group them into phrases, and finally how to talk to another person. Later, in elementary school, we are taught numbers and letters, how to recognize them, and how to use them to make calculations and spell words. As we grow up, we incorporate these lessons into a wide variety of real-life situations and circumstances. We also learn from our mistakes and successes, and then use them to create strategies for decision making that will result in better performance in our daily lives. Similarly, if a machine--or more accurately, a computer program--can improve how it performs a certain task based on past experience, then you can say that it has learned or that it has extracted knowledge from data.
The term machine learning was first defined by Arthur Samuel in 1959 as follows:
Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed.Based on that definition, he developed what later became known as the Samuel's checkers-player algorithm, whose purpose was to choose the next move based on a number of factors (the number and position of pieces--including kings--on each side). This algorithm was first executed by an IBM computer, which incorporated successful and winning moves into its program, and thus learned to play the game through experience. In other words, the computer learned winning strategies by repeatedly playing the game. On the other hand, a regular Checkers game that is set up with traditional programming cannot learn and improve through experience since it can only be given a fixed set of authorized moves and strategies.
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