Data Science Workshop: Machine Learning with Python
March 11 @ 2:00 pm – 5:00 pm
In the last few years, both industry and academia witnessed the rise of Machine Learning (ML) methods being applied in finance, marketing, retails, science, engineering, healthcare, and humanities. Learning how to apply ML methods to a domain specific application does not require a detailed knowledge about the inner machinery of these methods; however, one needs to learn the best practices and recommendations followed by the community.
In this workshop, after a brief overview on machine learning, we will focus on doing the hands-on training in applying ML models on various data types including image, text, and time series. We will work through the use cases of classification and regression problems and discuss where to apply supervised or unsupervised methods.
Objective of the workshop
- Understand supervised and unsupervised methods
- Choose correct metrics and sampling methods for classification vs regression problems
- Find out which features are important in a given dataset
- Learn to apply ML models such as Decision Trees, Random Forest, and Support Vector Machines
- Perform clustering and dimensionality reductions (PCA, t-SNE, K-means, etc.)
- Search the parameter space – hyperparameter optimization
What is needed? Laptop/Desktop with Internet connection
Duration: 3 hours
Programming Platform: On-line resource or Laptop. Instructions for on-line resources will be given in the workshop.
Prerequisite: Basic laptop usage. Basic knowledge of Python is helpful for doing the hands-on session.
Slides and materials: Will be provided in the workshop
This workshop is hosted by the Office of Advanced Research Computing (OARC), Rutgers University, organized by Bala Desinghu in collaboration with the Eastern Regional Network (ERN) and the New Jersey Big Data Alliance (NJBDA).
Participants are encouraged to attend with campus partners representing a variety of stakeholders for campus research and research computing (e.g., researchers, research computing professionals, students, staff, faculty, and practitioners, etc.).