Whether you're new to the field or seeking to expand your expertise, this workshop offers an exceptional opportunity to delve into supervised and unsupervised learning techniques while leveraging the cutting-edge computational resources of SMU ManeFrame 3. Dive into Scikit-Learn, a robust machine learning library, and explore its versatile tools for creating predictive models. From linear regression to support vector machines, you'll gain insights into how algorithms learn from data and make informed decisions. Venturing further, the workshop will unveil the realm of unsupervised learning, where participants will discover patterns and relationships within data without explicit labels. Leverage clustering techniques to segment data into meaningful groups and employ dimensionality reduction to distill essential information from complex datasets. Part 1: Intro to Machine Learning, scikit-learn package and data pre-processing, post-processing using evaluation metrics. Some supervised learning algorithm (regression analysis, distance-based algorithm, tree-based algorithm, Bayes algorithm, support vector machines).  Part 2: Ensemble approach in ML, Variable Selection & Dimensionality Reduction and Unsupervised Learning.