PythonDataScience/notebooks/05.00-Machine-Learning.ipynb

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    "# Machine Learning"
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    "<!--BOOK_INFORMATION-->\n",
    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
    "\n",
    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
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    "<!--NAVIGATION-->\n",
    "< [Further Resources](04.15-Further-Resources.ipynb) | [Contents](Index.ipynb) | [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb) >"
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    "In many ways, machine learning is the primary means by which data science manifests itself to the broader world.\n",
    "\n",
    "Machine learning is where these `computational and algorithmic skills` of data science meet the `statistical thinking` of data science, and the result is a collection of approaches to inference and data exploration that are `not about effective theory` so much as effective computation.\n",
    "\n",
    "The term \"machine learning\" is sometimes thrown around as if it is some kind of magic pill: \n",
    "\n",
    "> # *apply machine learning to your data, and all your problems will be solved!*\n",
    "\n",
    "As you might expect, the reality is rarely this simple.\n"
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    "\n",
    "While these methods can be incredibly powerful\n",
    "- to be effective they must be approached with a firm grasp of the strengths and weaknesses of each method, as well as - a grasp of general concepts such as bias and variance, overfitting and underfitting, and more.\n",
    "\n",
    "This chapter will dive into practical aspects of machine learning, primarily using Python's [Scikit-Learn](http://scikit-learn.org) package.\n",
    "\n",
    "This is not meant to be a comprehensive introduction to the field of machine learning; that is a large subject and necessitates a more technical approach than we take here.\n",
    "\n",
    "Nor is it meant to be a comprehensive manual for the use of the Scikit-Learn package (for this, you can refer to the resources listed in [Further Machine Learning Resources](05.15-Learning-More.ipynb)).\n"
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    "\n",
    "While these methods can be incredibly powerful\n",
    "- to be effective they must be approached with a firm grasp of the strengths and weaknesses of each method, as well as - a grasp of general concepts such as bias and variance, overfitting and underfitting, and more.\n",
    "\n",
    "This chapter will dive into practical aspects of machine learning, primarily using Python's [Scikit-Learn](http://scikit-learn.org) package.\n",
    "- This is not meant to be a comprehensive introduction to the field of machine learning; that is a large subject and necessitates a more technical approach than we take here.\n",
    "- Nor is it meant to be a comprehensive manual for the use of the Scikit-Learn package (for this, you can refer to the resources listed in [Further Machine Learning Resources](05.15-Learning-More.ipynb)).\n",
    "\n",
    "The goals of this chapter are:\n",
    "\n",
    "- To introduce the fundamental vocabulary and concepts of machine learning.\n",
    "- To introduce the Scikit-Learn API and show some examples of its use.\n",
    "- To take a deeper dive into the details of several of the most important machine learning approaches, and develop an intuition into how they work and when and where they are applicable.\n"
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    "<!--NAVIGATION-->\n",
    "< [Further Resources](04.15-Further-Resources.ipynb) | [Contents](Index.ipynb) | [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb) >"
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