This is the second part of a two-part tutorial series. Be sure to also register for Part One.

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!