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Machine Learning Workshop
This is the homepage for the Collaboratory Workshop, Machine Learning with Python. This workshop is offered by the QCBio Collaboratory (UCLA).
Table of contents
- Workshop description
- Day 1 - Jupyter and Machine Learning
- Day 2 - Classification and performance
- Day 3 - Cross-validation and regression
- Extra resources for after the workshop
- Technical requirements
- Material's license
In this workshop, we explore applications of Machine Learning to analyze biological data without the need of advanced programming skills. For example, Machine Learning techniques can be used to construct predictive models based on a set of training examples, to remove noise and spurious artifacts from data (e.g. photobleaching), or to help visualize trends within high dimensional datasets, etc. This workshop will cover the basic principles involved in the applications mentioned above, such as pattern recognition, linear and non-linear regression and cluster analysis. The workshop will be oriented towards hands-on activities, starting from the basics of how to load and prepare biological datasets in a Python environment. By the end of this workshop, students will be able to use Scikit-Learn’s documentation (and other libraries) to build models based on their own data, assess their performance and make new predictions.
Students are encouraged to attend to the Advanced Python and Modern Statistics workshops, although no advanced knowledge will be assumed.
Attendees should have a working copy of Python 3 with the following packages:
There are several ways you can contact us:
You can use the Issues Page of this repository to post questions, comments and suggestions. These will be visible to everyone.
You can contact me, Renaud, directly.
Also, check out the QCBio Collaboratory for more information and other workshops available to the UCLA community.
This material is shared under the GNU General Public License v3.0, please take a moment to read it. Permissions of this copyleft license are conditioned on making available complete source code of licensed works and modifications, which include larger works using a licensed work, under the same license. Copyright and license notices must be preserved. Contributors provide an express grant of patent rights.
Workshop - Machine Learning with Python Copyright (C) 2017 Thiago Schiavo Mosqueiro This material is a free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>.