Machine Learning using scikit-learn
NGCM Summer Academy 2017
Instructors
Christopher Fonnesbeck (Vanderbilt University Medical Center)
Skipper Seabold (Civis Analytics)
Outline
Thursday, June 29
09:30 - 10:45 (Chris Fonnesbeck)
Introduction to machine learning with scikit-learn
11:00 - 13:15 (Chris Fonnesbeck)
Unsupervised learning
- 1.c) PCA and transformations
- 1.d) Clustering
13:15 - 14:15 Lunch
14:15 - 16:00
Supervised Learning (Skipper Seabold)
16:15 - 17:30
Model selection (Skipper Seabold)
Friday, June 30
09:30 - 10:45
Supervised Learning (Chris Fonnesbeck)
- 3.a) Linear regression
- 3.b) Logistic regression
11:00 - 13:15 (Chris Fonnesbeck)
Ensemble Supervised Learning
- 3.c) Boosting
- 3.d) Gaussian processes
13:15 - 14:15 Lunch
14:15 - 16:00 (Skipper Seabold)
Advanced topics
- 4.a) Pipelining
- 4.b) Feature selection
16:15 - 17:30 (Skipper Seabold)
Advanced topics
- 4.c) Text feature extraction
- 4.d) Big Data Strategies
Prerequisites
This is an intermediate-level computing course, so some previous experience with Python is required. Some undergraduate-level statistics is also recommended.
Software Requirements
Python 3.5 or 3.6. We recommend installing the free Anaconda distribution of Python, available from Continuum Analytics.
The following packages should be installed on your system:
- jupyter
- ipython>=4.0
- numpy>=1.10
- pandas>=0.18
- scipy
- matplotlib
- scikit-learn
- dask
If you have installed Anaconda, most of these may already be available to you.
Getting this repository
git clone https://github.com/fonnesbeck/ngcm_sklearn_2017.git
If you are not familiar with Git and GitHub, you can simply download the zip file of the repository at the top of the main repository page.
Then, move to the directory created by the clone/zip file:
cd ngcm_sklearn_2017
and install everything using conda:
conda config --add channels conda-forge
conda env create -f environment.yml
This will create an environment called sklearn that includes the packages required for the course.
If you are not using the Anaconda Python distribution, you will need to manually install the packages listed in environment.yml using pip.
Which you probably don't want to do.
So install Anaconda.
To use the environment, you may type:
source activate sklearn