If you’re building models for data science, your feature sets can quickly become complicated and hard to understand. In this course, Reducing Complexity in Data in Microsoft Azure, you will learn how to reduce the complexity of feature sets, making models more understandable, more straightforward to build, and more robust. First, you will learn to understand feature set complexity and how it impacts your models. Next, you will discover a range of different techniques to improve the complexity of your feature sets. Finally, you will explore various advanced methods for feature set complexity reduction. When you are finished with this course, you will have the skills and knowledge needed to reduce the complexity of your models, and create more straightforward and manageable models, leading to better and more consistent insights into your data.
Author Name: Steph Locke
Author Description:
Steph is a data scientist by trade, and now runs two businesses that focus on helping businesses adopt data science and AI. She spends a large amount of her free time on improving the skills of the tech community at large, whether that’s via presenting, organizing conferences, or writing books. Due to her focus and community work, Steph is a Microsoft Most Valued Professional in AI and the Data Platform — one of only three people in the world to hold this combination.

Table of Contents

• Course Overview
  1min
• Understanding How Feature Set Complexity Impacts Model Quality
  29mins
• Applying Criteria-based Feature Reduction Techniques
  28mins
• Using Principal Component Analysis to Reduce Numeric Feature Sets
  33mins
• Processing Categorical or Text Feature Sets
  22mins
• Going beyond PCA to Reduce Complexity in Numeric Feature Sets
  19mins