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‎README.md‎

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@@ -17,6 +17,7 @@ The notebooks highlight techniques such as:
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The notebooks can be accessed through:
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* [H2O Aquarium (Recommended)](https://github.com/jphall663/interpretable_machine_learning_with_python#h2o-aquarium-recommended)
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* [Virtualenv (Advanced)]()
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* [Docker container (Advanced)](https://github.com/jphall663/interpretable_machine_learning_with_python#docker-installation)
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* [Manual installation (Advanced)](https://github.com/jphall663/interpretable_machine_learning_with_python#manual-installation)
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11. Click `End Lab` when you are finished.
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### Virtualenv Installation
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1. Install [Git](https://git-scm.com/downloads).
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2. Clone this repository with the examples.</br>
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`$ git clone https://github.com/jphall663/interpretable_machine_learning_with_python.git`
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3. Install Anaconda Python 5.1.0 from the [Anaconda archives](https://repo.continuum.io/archive/) and add it to your system path.
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4. Change directories into the cloned repository.</br>
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`$ cd interpretable_machine_learning_with_python`
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5. Create a Python 3.6 virtual environment.</br>
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`$ virtualenv -p /path/to/anaconda3/bin/python3.6 env_iml`.
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6. Activate the virtual environment.</br>
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`$ source env_iml/bin/activate`
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7. Install the correct packages for the example notebooks.</br>
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`$ pip install -r requirements.txt`
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8. Start Jupyter.</br>
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`$ jupyter notebook`
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### Docker Installation
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A Dockerfile is provided to build a docker container with all necessary packages and dependencies. This is a way to use these examples if you are on Mac OS X, \*nix, or Windows 10. To do so:
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1. Install and start [docker](https://www.docker.com/).
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1. Clone the example notebooks
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2. Install and start [docker](https://www.docker.com/).
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From a terminal:
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