Machine learning is a subfield of artificial intelligence (AI) concerned with algorithms
that allow computers to learn. What this means, in most cases, is that an
algorithm is given a set of data and infers information about the properties of the
data—and that information allows it to make predictions about other data that it
might see in the future. This is possible because almost all nonrandom data contains
patterns, and these patterns allow the machine to generalize. In order to generalize, it
trains a model with what it determines are the important aspects of the data.
To understand how models come to be, consider a simple example in the otherwise
complex field of email filtering. Suppose you receive a lot of spam that contains the
words “online pharmacy.” As a human being, you are well equipped to recognize patterns,
and you quickly determine that any message with the words “online pharmacy” is spam and should be moved directly to the trash. This is a generalization—you have,
in fact, created a mental model of what is spam. After you report several of these
messages as spam, a machine-learning algorithm designed to filter spam should be
able to make the same generalization.
There are many different machine-learning algorithms, all with different strengths
and suited to different types of problems. Some, such as decision trees, are transparent,
so that an observer can totally understand the reasoning process undertaken by
the machine. Others, such as neural networks, are blackbox , meaning that they produce
an answer, but it’s often very difficult to reproduce the reasoning behind it.
Many machine-learning algorithms rely heavily on mathematics and statistics.
According to the definition I gave earlier, you could even say that simple correlation
analysis and regression are both basic forms of machine learning. This book does not
assume that the reader has a lot of knowledge of statistics, so I have tried to explain
the statistics used in as straightforward a manner as possible.
that allow computers to learn. What this means, in most cases, is that an
algorithm is given a set of data and infers information about the properties of the
data—and that information allows it to make predictions about other data that it
might see in the future. This is possible because almost all nonrandom data contains
patterns, and these patterns allow the machine to generalize. In order to generalize, it
trains a model with what it determines are the important aspects of the data.
To understand how models come to be, consider a simple example in the otherwise
complex field of email filtering. Suppose you receive a lot of spam that contains the
words “online pharmacy.” As a human being, you are well equipped to recognize patterns,
and you quickly determine that any message with the words “online pharmacy” is spam and should be moved directly to the trash. This is a generalization—you have,
in fact, created a mental model of what is spam. After you report several of these
messages as spam, a machine-learning algorithm designed to filter spam should be
able to make the same generalization.
There are many different machine-learning algorithms, all with different strengths
and suited to different types of problems. Some, such as decision trees, are transparent,
so that an observer can totally understand the reasoning process undertaken by
the machine. Others, such as neural networks, are blackbox , meaning that they produce
an answer, but it’s often very difficult to reproduce the reasoning behind it.
Many machine-learning algorithms rely heavily on mathematics and statistics.
According to the definition I gave earlier, you could even say that simple correlation
analysis and regression are both basic forms of machine learning. This book does not
assume that the reader has a lot of knowledge of statistics, so I have tried to explain
the statistics used in as straightforward a manner as possible.
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