Reducing Complexity in Data in Microsoft Azure

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Steph Locke

2:14:01

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1. Course Overview

1. Course Overview.mp4

01:26

2. Understanding How Feature Set Complexity Impacts Model Quality

1. Introduction and Module Overview.mp4

06:02

2. Feature Set Complexity.mp4

02:03

3. Sources of Model Error.mp4

03:26

4. Measuring and Detecting Problems Due to Feature Set Complexity.mp4

02:46

5. How Feature Set Complexity Impacts Model Interpretability.mp4

05:50

6. Demo.mp4

08:20

7. Summary.mp4

01:15

3. Applying Criteria-based Feature Reduction Techniques

1. Introduction and Module Overview.mp4

03:29

2. Reasons For Feature Elimination.mp4

01:34

3. Univariate Techniques.mp4

04:43

4. How Statistical Tests Work.mp4

04:35

5. Bivariate Techniques.mp4

05:13

6. Multivariate Techniques.mp4

03:07

7. Demo.mp4

04:19

8. Summary.mp4

01:07

4. Using Principal Component Analysis to Reduce Numeric Feature Sets

1. Introduction and Module Overview.mp4

03:21

2. PCA.mp4

03:17

3. How PCA Works.mp4

13:10

4. KPCA.mp4

04:04

5. PCA Limitations.mp4

03:18

6. Demo.mp4

04:45

7. Summary.mp4

01:44

5. Processing Categorical or Text Feature Sets

1. Introduction and Module Overview.mp4

01:47

2. How Can You Process Categorical or Text Feature Sets.mp4

05:07

3. LDA.mp4

05:09

4. Dictionary Learning.mp4

03:21

5. t-SNE.mp4

02:01

6. Demo.mp4

03:46

7. Summary.mp4

00:54

6. Going beyond PCA to Reduce Complexity in Numeric Feature Sets

1. Introduction and Module Overview.mp4

01:28

2. k-means Model Stacking.mp4

02:42

3. How k-means Works.mp4

03:55

4. Autoencoders.mp4

03:41

5. Manifold Learning.mp4

02:44

6. Demo.mp4

03:19

7. Summary.mp4

01:13

Description

In machine learning, feature sets can quickly become complicated and unwieldy. This course will give you the skills needed to reduce the complexity of your feature sets to help ensure you get better and more consistent insights into your data.

What You'll Learn?

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.

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Microsoft Azure

Steph Locke

Instructor's Courses

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.

Pluralsight

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Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.