Machine learning (ML) extracts knowledge from data and focuses on prediction. ML learns from our data how to make decisions for future observations. It is widely used and common in everyday interactions – on Facebook, Google, Amazon, or your favorite automated teller machine (ATM). Equally important are applications in science, such as personalized cancer treatment, medical diagnoses, and drug discovery. ML is essential in data driven sciences. This machine learning workshop series is composed of 4 sessions of hands-on practice with the Python scikit-learn library.
At the end of this course, participants will be able to:
Describe the types of machine learning
Describe the basics of supervised learning
Build regression, classification, and clustering models to model data
Apply and evaluate a machine learning algorithm
This workshop is aimed at graduate students and other researchers who would like to learn more about machine learning for biomedical data. This workshop is open to those from neighboring institutions.
Prerequisite: Competence with Python and the basics of the Pandas and NumPy libraries.
Holt Conference Room, 10 Discovery Drive, Farmington, Connecticut.
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Requirements: Participants must bring a laptop with a
Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.). They should have the most recent version of
Python installed (see below).
Accessibility: We are committed to making this workshop
accessible to everybody.
The workshop organizers have checked that:
The room is wheelchair / scooter accessible.
Accessible restrooms are available.
Materials will be provided in advance of the workshop and
large-print handouts are available if needed by notifying the
organizers in advance. If we can help making learning easier for
you (e.g. sign-language interpreters, lactation facilities) please
get in touch (using contact details below) and we will
attempt to provide them.
Python is a popular language for
research computing, and great for general-purpose programming as
well. Installing all of its research packages individually can be
a bit difficult, so we recommend
an all-in-one installer.
Regardless of how you choose to install it,
please make sure you install Python version 3.x
(e.g., 3.6 is fine).
We will teach Python using the Jupyter Notebook,
a programming environment that runs in a web browser (Jupyter Notebook will be installed by Anaconda). For this to work you will need a reasonably
up-to-date browser. The current versions of the Chrome, Safari and
Firefox browsers are all
(some older browsers, including Internet Explorer version 9
and below, are not).
Download the Anaconda Installer with Python 3 for Linux.
(The installation requires using the shell. If you aren't
comfortable doing the installation yourself
stop here and request help at the workshop.)
Open a terminal window and navigate to the directory where
the executable is downloaded (e.g., `cd ~/Downloads`).
and then press
Tab to autocomplete the full file name. The name of
file you just downloaded should appear.
You will follow the text-only prompts.
To move through the text, press Spacebar.
Type yes and press enter to approve the license.
Press Enter to approve the default location
for the files.
Type yes and press Enter
to prepend Anaconda to your PATH
(this makes the Anaconda distribution the default Python).