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Machine Learning For Dummies by Luca Massaron, John Paul Mueller

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Chapter 2

Learning in the Age of Big Data

IN THIS CHAPTER

Understanding the essentials of big data

Locating big data sources

Considering how statistics and big data work together in machine learning

Defining the role of algorithms in machine learning

Determining how training works with algorithms in machine learning

Computers manage data through applications that perform tasks using algorithms of various sorts. A simple definition of an algorithm is a systematic set of operations to perform on a given data set — essentially a procedure. The four basic data operations are Create, Read, Update, and Delete (CRUD). This set of operations may not seem complex, but performing these essential tasks is the basis of everything you do with a computer. As the dataset becomes larger, the computer can use the algorithms found in an application to perform more work. The use of immense datasets, known as big data, enables a computer to perform work based on pattern recognition in a nondeterministic manner. In short, to create a computer setup that can learn, you need a dataset large enough for the algorithms to manage in a manner that allows for pattern recognition, and this pattern recognition needs to use a simple subset to make predictions (statistical analysis) of the dataset as a whole.

Big data exists in many places today. Obvious sources are online databases, such as those created by vendors to track consumer purchases. However, you find many non-obvious data sources, too, and often ...

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