Читать книгу Machine Learning For Dummies - John Paul Mueller, John Mueller Paul, Luca Massaron - Страница 29

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 dataset — 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. Algorithms determine how a machine interprets big data. The algorithm used to perform machine learning affects the outcome of the learning process and, therefore, the results you get. 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 these non-obvious sources provide the greatest resources for doing something interesting. Finding appropriate sources of big data lets you create machine learning scenarios in which a machine can learn in a specified manner and produce a desired result.

Statistics, one of the methods of machine learning that you consider in this book, is a method of describing problems using math. By combining big data with statistics, you can create a machine learning environment in which the machine considers the probability of any given event. However, saying that statistics is the only machine learning method is incorrect. This chapter also introduces you to the other forms of machine learning currently in place.

Before an algorithm can do much in the way of machine learning, you must train it. The training process modifies how the algorithm views big data. It’s essential to understand that training is actually using a subset of the data as a method for creating the patterns that the algorithm needs to recognize specific cases from the more general cases that you provide as part of the training.