IS&T RCS Tutorial - Machine Learning with Python scikit-learn, Part Two (Hands-on)

This session was rescheduled from Feb 6 due to technical issues. 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 conda environment file with all necessary packages will be shared before the session, along with activation instructions. Prerequisites: Experience with Python programming using Jupyter Notebook and familiarity with libraries like NumPy, Pandas, and Matplotlib. Get ready to explore the capabilities of Scikit-Learn and start building practical machine learning solutions!