- Andreas Mueller @amuellerml - Columbia University; Introduction to Machine Learning with Python
If you have a GitHub account, it is probably most convenient if you clone or fork the GitHub repository. You can clone the repository by running:
git clone https://github.com/amueller/jupytercon2017.git
If you are not familiar with git or don’t have an GitHub account, you can download the repository as a .zip file by heading over to the GitHub repository (https://github.com/amueller/jupytercon2017) in your browser and click the green “Download” button in the upper right.
Please note that we may add and improve the material until shortly before the tutorial session, and we recommend you to update your copy of the materials one day before the tutorials. If you have an GitHub account and cloned the repository via GitHub, you can sync your existing local repository with:
git pull origin masterIf you don’t have a GitHub account, you may have to re-download the .zip archive from GitHub.
This tutorial will require recent installations of
The last one is important, you should be able to type:
jupyter notebook
in your terminal window and see the notebook panel load in your web browser. Try opening and running a notebook from the material to see check that it works.
For users who do not yet have these packages installed, a relatively painless way to install all the requirements is to use a Python distribution such as Anaconda CE, which includes the most relevant Python packages for science, math, engineering, and data analysis; Anaconda can be downloaded and installed for free including commercial use and redistribution. The code examples in this tutorial should be compatible to Python 2.7, Python 3.4-3.6.
After obtaining the material, we strongly recommend you to open and execute
the Jupyter Notebook jupter notebook check_env.ipynb that is located at the
top level of this repository. Inside the repository, you can open the notebook
by executing
jupyter notebook check_env.ipynbinside this repository. Inside the Notebook, you can run the code cell by clicking on the "Run Cells" button as illustrated in the figure below:
Finally, if your environment satisfies the requirements for the tutorials, the executed code cell will produce an output message as shown below:
Although not required, we also recommend you to update the required Python packages to their latest versions to ensure best compatibility with the teaching material. Please upgrade already installed packages by executing
pip install [package-name] --upgrade- or
conda update [package-name]
t.b.a.