Intermediate Machine Learning with scikit-learn

Cross validation, Parameter Tuning, Pandas Interoperability, and Missing Values

By Thomas J. Fan

Scikit-learn is a machine learning library in Python that is used by many data science practitioners. In this workshop, we will learn about cross validation, tuning machine learning algorithms, pandas interoperability, and handling missing values. Cross validation enables us to evaluate our machine learning models by splitting our data into multiple training and testing datasets. Hyper-parameter tuning helps us find parameter combinations that are suited for your data. Scikit-learn's ColumnTransformer is used to handle heterogenous data provided as a panda's DataFrame. Furthermore, scikit-learn provides univariate and K-Nearest Neighbor transformers for imputing missing values in our datasets.

Obtaining the Material

With git

The most convenient way to download the material is with git:

git clone https://github.com/thomasjpfan/ml-workshop-intermediate-1-of-2

Please note that I may add and improve the material until shortly before the session. You can update your copy by running:

git pull origin master

Download zip

If you are not familiar with git, you can download this repository as a zip file at: github.com/thomasjpfan/ml-workshop-intermediate-1-of-2/archive/master.zip. Please note that I may add and improve the material until shortly before the session. To update your copy please re-download the material a day before the session.

Running the notebooks

Local Installation

Local installation requires conda to be installed on your machine. The simplest way to install conda is to install miniconda by using an installer for your operating system provided at docs.conda.io/en/latest/miniconda.html. After conda is installed, navigate to this repository on your local machine:

cd ml-workshop-intermediate-1-of-2

Then download and install the dependencies:

conda env create -f environment.yml

This will create a virtual environment named ml-workshop-intermediate-1-of-2. To activate this environment:

conda activate ml-workshop-intermediate-1-of-2

Finally, to start jupyterlab run:

jupyter lab

This should open a browser window with the jupterlab interface.

Run with Google's Colab

If you have any issues with installing conda or running jupyter on your local computer, then you can run the notebooks on Google's Colab:

License

This repo is under the MIT License.

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