Reducing Complexity in Data in Microsoft Azure

سرفصل های دوره

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.

1. Course Overview

1. Course Overview

2. Understanding How Feature Set Complexity Impacts Model Quality

1. Introduction and Module Overview

2. Feature Set Complexity

3. Sources of Model Error

4. Measuring and Detecting Problems Due to Feature Set Complexity

5. How Feature Set Complexity Impacts Model Interpretability

6. Demo

7. Summary

3. Applying Criteria-based Feature Reduction Techniques

1. Introduction and Module Overview

2. Reasons For Feature Elimination

3. Univariate Techniques

4. How Statistical Tests Work

5. Bivariate Techniques

6. Multivariate Techniques

7. Demo

8. Summary

4. Using Principal Component Analysis to Reduce Numeric Feature Sets

1. Introduction and Module Overview

2. PCA

3. How PCA Works

4. KPCA

5. PCA Limitations

6. Demo

7. Summary

5. Processing Categorical or Text Feature Sets

1. Introduction and Module Overview

2. How Can You Process Categorical or Text Feature Sets

3. LDA

4. Dictionary Learning

5. t-SNE

6. Demo

7. Summary

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

1. Introduction and Module Overview

2. k-means Model Stacking

3. How k-means Works

4. Autoencoders

5. Manifold Learning

6. Demo

7. Summary

189,000 تومان

افزودن به سبد خرید

خرید دانلودی فوری

در این روش نیاز به افزودن محصول به سبد خرید و تکمیل اطلاعات نیست و شما پس از وارد کردن ایمیل خود و طی کردن مراحل پرداخت لینک های دریافت محصولات را در ایمیل خود دریافت خواهید کرد.

تولید کننده: PluralSight