mhjensen
158 lines
6.2 KiB
HTML
158 lines
6.2 KiB
HTML
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<html>
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<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
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<meta name="description" content="Data Analysis and Machine Learning: Introduction and Representing data">
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<title>Data Analysis and Machine Learning: Introduction and Representing data</title>
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<link href="https://netdna.bootstrapcdn.com/bootstrap/3.1.1/css/bootstrap.min.css" rel="stylesheet">
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<a class="navbar-brand" href="How2ReadData-bs.html">Data Analysis and Machine Learning: Introduction and Representing data</a>
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<ul class="nav navbar-nav navbar-right">
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<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
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<!-- navigation toc: --> <li><a href="#___sec0" style="font-size: 80%;">What is Machine Learning?</a></li>
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<!-- navigation toc: --> <li><a href="._How2ReadData-bs003.html#___sec2" style="font-size: 80%;">Different algorithms</a></li>
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<!-- navigation toc: --> <li><a href="._How2ReadData-bs004.html#___sec3" style="font-size: 80%;">Software and needed installations</a></li>
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<!-- navigation toc: --> <li><a href="._How2ReadData-bs005.html#___sec4" style="font-size: 80%;">Python installers</a></li>
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<!-- navigation toc: --> <li><a href="._How2ReadData-bs006.html#___sec5" style="font-size: 80%;">Installing R and C++</a></li>
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<!-- navigation toc: --> <li><a href="._How2ReadData-bs007.html#___sec6" style="font-size: 80%;">Introduction to Jupyter notebook and available tools</a></li>
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<!-- navigation toc: --> <li><a href="._How2ReadData-bs008.html#___sec7" style="font-size: 80%;">Representing data, overarching aims</a></li>
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<!-- navigation toc: --> <li><a href="._How2ReadData-bs009.html#___sec8" style="font-size: 80%;">Representing data, more examples</a></li>
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<p> </p><p> </p><p> </p> <!-- add vertical space -->
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<a name="part0001"></a>
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<!-- !split -->
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<h2 id="___sec0" class="anchor">What is Machine Learning? </h2>
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<p>
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Machine learning is the science of giving computers the ability to
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learn without being explicitly programmed. The idea is that there
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exist generic algorithms which can be used to find patterns in a broad
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class of data sets without having to write code specifically for each
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problem. The algorithm will build its own logic based on the data.
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<p>
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Machine learning is a subfield of computer science, and is closely
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related to computational statistics. It evolved from the study of
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pattern recognition in artificial intelligence (AI) research, and has
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made contributions to AI tasks like computer vision, natural language
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processing and speech recognition. It has also, especially in later
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years, found applications in a wide variety of other areas, including
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bioinformatics, economy, physics, finance and marketing.
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<p>
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<p>
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<li><a href="._How2ReadData-bs000.html">«</a></li>
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<li><a href="._How2ReadData-bs000.html">1</a></li>
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<li><a href="._How2ReadData-bs002.html">3</a></li>
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<li><a href="._How2ReadData-bs004.html">5</a></li>
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<li><a href="._How2ReadData-bs005.html">6</a></li>
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<li><a href="._How2ReadData-bs006.html">7</a></li>
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<li><a href="._How2ReadData-bs007.html">8</a></li>
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<li><a href="._How2ReadData-bs008.html">9</a></li>
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<li><a href="._How2ReadData-bs009.html">10</a></li>
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<li><a href="._How2ReadData-bs002.html">»</a></li>
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