Python Machine Learning for Beginners: Data Science and Machine Learning with Python.Technology, Principles, and Applications. - Couverture rigide

Treu, Peter

9781801449816: Python Machine Learning for Beginners: Data Science and Machine Learning with Python.Technology, Principles, and Applications.

Couverture rigide

ISBN 10 : 1801449813 ISBN 13 : 9781801449816

Editeur : Charlie Creative Lab, 2020

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Do you Want to learn more about Python Machine Learning ? .... then read on.

Machine learning stems from this question: Can a computer go beyond anything we can order to do and learn by itself to do a specific task? Can a laptop surprise us? Instead of having programmers carefully and manually writing a set of data processing rules, can a computer automatically learn these rules by merely looking at the data?

This question paves the way for a new programming paradigm. In classical programming, on which symbolic artificial intelligence is based, human beings insert rules (the program) and the data to be processed according to these rules and obtain answers. Humans enter data and expected responses based on that data with machine learning, and the computer identifies the practices. These rules can then be applied to other data to produce different, original answers.

A machine learning system is trained and not programmed. He is presented with numerous examples relevant to a given task. In these examples, he finds a statistical structure that ultimately allows him to produce the rules for the task's automation. For example, to automate tagging vacation photographs, many examples of images already tagged by humans could be presented to a machine learning system. The system would be tasked with learning the statistical rules based on associating individual images with specific tags.

Machine learning is closely related to statistics, but it differs from them in many important ways. Unlike statistics, machine learning tends to operate with large and complex datasets (such as a dataset of millions of images, each consisting of tens of thousands of pixels) for which classical statistical ysis such as Bayesian ysis would not be usable. . As a result, machine learning, and especially deep learning, exhibits somewhat limited mathematical theory - sometimes too much - and is more technical than mathematical.

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