Machine learning is the new frontier for data science. Machine learning can be used to write programs that perform a repetitive task by using the power of computers to learn on their own. The concept behind machine learning is so simple that it should be easy to describe and understand. You may wonder why you would want your code to learn from previous examples, but there are many reasons why people use machine learning regularly in programming, business, finance and other applications.
The field of machine learning has been evolving at a rapid pace over the last few years. A machine learning system (ML) is intelligence based on sets of rules and performs tasks using these rules to learn. For instance, a machine learning system could be useful in online advertising where it tries to predict the interests of users based on a set of factors such as their IP address, URLs visited, and other information related to that user's history.
Machine learning is an idea that's been around for some time, as evidenced by the World War II Enigma Machine. But it's only recently that computers have become powerful enough to automate the application of sophisticated mathematical computations to massive data sets.
Machine learning is the science of getting computers to act without being explicitly programmed. In this tutorial, you'll learn about the most popular and effective types of machine learning algorithms, and how you can use them to give your project that competitive edge. In order to get the full benefit of this article, some prior knowledge of Python and machine learning is assumed.
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