Building Features from Nominal and Numeric Data in Microsoft Azure

سرفصل های دوره

Applying statistical techniques to your data within Azure Machine Learning Service will often boost model performance. This course will teach you the basics of data cleansing, including basic syntax and functions.

1. Course Overview

1. Course Overview

2. Setting the Stage

01. Module Overview

02. Is This Course for You

03. Skills Recommended for This Course

04. Measures of Central Tendency

05. Measures of Variability

06. Modality and Skewness

07. Kurtosis

08. Gaussian Distributions

09. Calculating the Mean, Median, and Mode

10. Summary

3. Approaching Normalization and Standardization

1. Introduction

2. Machine Learning Process Distilled

3. Outlier Detection and Imputation

4. Demo - Outlier Detection in Python

5. Demo - Imputation in Python

6. Standardization and Normalization

7. Demo - Normalize and Standardize in Python

8. Summary

4. Defining Normalization and Standardization Techniques

01. Module Overview

02. Common Scaling Approaches

03. Demo - Common Scaling Approaches

04. Binning

05. Demo - Binning

06. Heteroscedasticity

07. Demo - Correcting Heteroscedasticity

08. Z-score

09. Demo - Z-score

10. Summary

5. Leveraging Nominal Data in Machine Learning

1. Module Overview

2. Data Types in Statistics

3. Data Measurement Scales

4. Problems with Categorical Data

5. One-hot Encoding

6. Demo - Label and One-hot Encoding

7. Demo - Label Encoding and XGBoost

8. Summary

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تولید کننده: PluralSight