This workshop is tailored to provide attendees with a hands-on approach to address common challenges encountered in machine learning. Participants will learn practical techniques such as missing value handling, outlier detection and mitigation, and feature engineering. The workshop will delve into strategies for dealing with imbalanced datasets, a common hurdle, and arm the attendees with effective solutions. We will also explore the application of dimensionality reduction methods for efficiently handling high-dimensional data. Additionally, the workshop will shed light on the concept of regularization to overcome underfitting and overfitting issues in your ML models.
Please note: Introductory background in ML is required. Please watch Fall '23 workshop #1 to gain basic understanding.