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IS&T RCS Tutorial - Machine Learning with Python scikit-learn, Part One (Hands‐on)
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Boston University
Boston, Massachusetts
Listed 4 hours ago
Date Posted January 16, 2026 12:33 PM
Event Details
Format
inperson
Location and Room
Online over Zoom After you register, you will be sent a calendar invite that includes the Zoom link.
Address
Boston, Massachusetts
Description
This is the first part of a two-part tutorial series. Be sure to also register for Part Two to continue building your knowledge.
What to Expect: This session introduces Scikit-Learn, a powerful Python library for machine learning. Scikit-Learn supports supervised and unsupervised learning and offers tools for: Data preprocessing Model fitting Model selection Evaluation And much more
Through hands-on exercises with real datasets, you'll learn to develop models using modern algorithms, including: Linear regression Decision trees and random forests K-means clustering Dimensionality reduction
We'll also provide an overview of the general machine learning workflow and wrap up with guidance on further ML resources.
Preparation: If Python is not installed on your machine, follow these instructions.
A cond...
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Help others discover this itemIS&T RCS Tutorial - Machine Learning with Python scikit-learn, Part One (Hands‐on)
Details
Events
General
Boston University
Boston, Massachusetts
Listed 4 hours ago
Date Posted January 16, 2026 12:33 PM
Event Details
Format
inperson
Location and Room
Online over Zoom After you register, you will be sent a calendar invite that includes the Zoom link.
Address
Boston, Massachusetts
Description
This is the first part of a two-part tutorial series. Be sure to also register for Part Two to continue building your knowledge.
What to Expect: This session introduces Scikit-Learn, a powerful Python library for machine learning. Scikit-Learn supports supervised and unsupervised learning and offers tools for: Data preprocessing Model fitting Model selection Evaluation And much more
Through hands-on exercises with real datasets, you'll learn to develop models using modern algorithms, including: Linear regression Decision trees and random forests K-means clustering Dimensionality reduction
We'll also provide an overview of the general machine learning workflow and wrap up with guidance on further ML resources.
Preparation: If Python is not installed on your machine, follow these instructions.
A cond...