02_machine_learning_setup.ipynb

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   "source": [
    "# Setting up Python for machine learning: scikit-learn and IPython Notebook\n"
   ]
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   "source": [
    "## Agenda\n",
    "\n",
    "- What are the benefits and drawbacks of scikit-learn?\n",
    "- How do I install scikit-learn?\n",
    "- How do I use the IPython Notebook?\n",
    "- What are some good resources for learning Python?"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![scikit-learn algorithm map](images/02_sklearn_algorithms.png)"
   ]
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    "## Benefits and drawbacks of scikit-learn\n",
    "\n",
    "### Benefits:\n",
    "\n",
    "- **Consistent interface** to machine learning models\n",
    "- Provides many **tuning parameters** but with **sensible defaults**\n",
    "- Exceptional **documentation**\n",
    "- Rich set of functionality for **companion tasks**\n",
    "- **Active community** for development and support\n",
    "\n",
    "### Potential drawbacks:\n",
    "\n",
    "- Harder (than R) to **get started with machine learning**\n",
    "- Less emphasis (than R) on **model interpretability**\n",
    "\n",
    "### Further reading:\n",
    "\n",
    "- Ben Lorica: [Six reasons why I recommend scikit-learn](http://radar.oreilly.com/2013/12/six-reasons-why-i-recommend-scikit-learn.html)\n",
    "- scikit-learn authors: [API design for machine learning software](http://arxiv.org/pdf/1309.0238v1.pdf)\n",
    "- Data School: [Should you teach Python or R for data science?](http://www.dataschool.io/python-or-r-for-data-science/)"
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "![scikit-learn logo](images/02_sklearn_logo.png)"
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   "source": [
    "## Installing scikit-learn\n",
    "\n",
    "**Option 1:** [Install scikit-learn library](http://scikit-learn.org/stable/install.html) and dependencies (NumPy and SciPy)\n",
    "\n",
    "**Option 2:** [Install Anaconda distribution](https://www.continuum.io/downloads) of Python, which includes:\n",
    "\n",
    "- Hundreds of useful packages (including scikit-learn)\n",
    "- IPython and IPython Notebook\n",
    "- conda package manager\n",
    "- Spyder IDE"
   ]
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   "source": [
    "![IPython header](images/02_ipython_header.png)"
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    "## Using the IPython Notebook\n",
    "\n",
    "### Components:\n",
    "\n",
    "- **IPython interpreter:** enhanced version of the standard Python interpreter\n",
    "- **Browser-based notebook interface:** weave together code, formatted text, and plots\n",
    "\n",
    "### Installation:\n",
    "\n",
    "- **Option 1:** Install [IPython](http://ipython.org/install.html) and the [notebook](https://jupyter.readthedocs.io/en/latest/install.html)\n",
    "- **Option 2:** Included with the Anaconda distribution\n",
    "\n",
    "### Launching the Notebook:\n",
    "\n",
    "- Type **ipython notebook** at the command line to open the dashboard\n",
    "- Don't close the command line window while the Notebook is running\n",
    "\n",
    "### Keyboard shortcuts:\n",
    "\n",
    "**Command mode** (gray border)\n",
    "\n",
    "- Create new cells above (**a**) or below (**b**) the current cell\n",
    "- Navigate using the **up arrow** and **down arrow**\n",
    "- Convert the cell type to Markdown (**m**) or code (**y**)\n",
    "- See keyboard shortcuts using **h**\n",
    "- Switch to Edit mode using **Enter**\n",
    "\n",
    "**Edit mode** (green border)\n",
    "\n",
    "- **Ctrl+Enter** to run a cell\n",
    "- Switch to Command mode using **Esc**\n",
    "\n",
    "### IPython and Markdown resources:\n",
    "\n",
    "- [nbviewer](http://nbviewer.jupyter.org/): view notebooks online as static documents\n",
    "- [IPython documentation](http://ipython.readthedocs.io/en/stable/): focuses on the interpreter\n",
    "- [IPython Notebook tutorials](http://jupyter.readthedocs.io/en/latest/content-quickstart.html): in-depth introduction\n",
    "- [GitHub's Mastering Markdown](https://guides.github.com/features/mastering-markdown/): short guide with lots of examples"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resources for learning Python\n",
    "\n",
    "- [Codecademy's Python course](https://www.codecademy.com/learn/python): browser-based, tons of exercises\n",
    "- [DataQuest](https://www.dataquest.io/): browser-based, teaches Python in the context of data science\n",
    "- [Google's Python class](https://developers.google.com/edu/python/): slightly more advanced, includes videos and downloadable exercises (with solutions)\n",
    "- [Python for Informatics](http://www.pythonlearn.com/): beginner-oriented book, includes slides and videos"
   ]
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