Merge pull request #49096 from PhilKang0704/broken-link-fix-softchris

Broken links fixing - softchris

Author: v-ccolin

learn-pr/reactors/join-clean-data-deep-dive/includes/1-introduction.md

@@ -3,7 +3,7 @@ As a general rule, data scientists assume that about 80 percent of the time and
 
 This section makes extensive use of pandas, the principal Python library for data handling and manipulation. This section assumes some familiarity with pandas and basic Python skills.
 
-To provide an experience more like real-world data science, we'll use real data gathered from the [U.S. Department of Agriculture National Nutrient Database for Standard Reference](https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-nutrition-research-center/nutrient-data-laboratory/docs/usda-national-nutrient-database-for-standard-reference/?azure-portal=true).
+To provide an experience more like real-world data science, we'll use real data gathered from the [U.S. Department of Agriculture National Nutrient Database for Standard Reference](https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-nutrition-research-center/?azure-portal=true).
 
 ## Learning objectives
 

learn-pr/reactors/principal-component-analysis-food-composition/includes/2-pca-theory-download-data.md

@@ -29,7 +29,7 @@ import matplotlib.pyplot as plt
 %matplotlib inline
 ```
 
-The dataset we'll use here's the same one that's drawn from the [U.S. Department of Agriculture National Nutrient Database for Standard Reference](https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-nutrition-research-center/nutrient-data-laboratory/docs/usda-national-nutrient-database-for-standard-reference/?azure-portal=true) that you prepared in the preceding module.
+The dataset we'll use here's the same one that's drawn from the [U.S. Department of Agriculture National Nutrient Database for Standard Reference](https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-nutrition-research-center/?azure-portal=true) that you prepared in the preceding module.
 
 Remember to set the encoding to `latin1` (for µg):
 

learn-pr/reactors/unsupervised-learning-clustering/includes/2-load-prepare-data.md

@@ -1,4 +1,4 @@
-To illustrate k-means clustering in action, we'll use the familiar [U.S. Department of Agriculture National Nutrient Database for Standard Reference](https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-nutrition-research-center/nutrient-data-laboratory/docs/usda-national-nutrient-database-for-standard-reference/?azure-portal=true) dataset that you used in sections 1.1 through 1.3. Note that the path name is case sensitive.
+To illustrate k-means clustering in action, we'll use the familiar [U.S. Department of Agriculture National Nutrient Database for Standard Reference](https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-nutrition-research-center/?azure-portal=true) dataset that you used in sections 1.1 through 1.3. Note that the path name is case sensitive.
 
 ```python
 import pandas as pd