4.datasci/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."
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    "Machine learning is \n",
    "- where these `computational and algorithmic skills` of **data science** meet the `statistical thinking` of **data science**, \n",
    "- the result is a collection of approaches to inference and data exploration \n",
    " - are `not about effective theory` so much as effective computation."
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    "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",
    "To be effective, they must be approached with \n",
    "- a firm grasp of the strengths and weaknesses of each method, \n",
    "- a firm grasp of general concepts \n",
    "    - such as bias and variance, overfitting and underfitting, and more.\n"
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    "We 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; \n",
    "    - 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 \n",
    "    - for this, you can refer to the resources listed in [Further Machine Learning Resources](05.15-Learning-More.ipynb)).\n"
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    "The goals of this section 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 \n",
    "    - 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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