fix typos plus a couple style fixes (#383)

9-Real-World/1-Applications/README.md

@@ -32,7 +32,7 @@ http://www.brightwoodventures.com/evaluating-fund-performance-using-regression/
 
 ## 🎓 Education
 
-The educational sector is also a very interesting area where ML can be applied. There are interesting problems to be tackled such as detecting cheating on tests or essays or managing bias in the correction process, unintentional or not.
+The educational sector is also a very interesting area where ML can be applied. There are interesting problems to be tackled such as detecting cheating on tests or essays or managing bias, unintentional or not, in the correction process.
 
 ### Predicting student behavior
 
@@ -98,9 +98,9 @@ https://www.frontiersin.org/articles/10.3389/fict.2018.00006/full
 
 ### Motion sensing of animals
 
-While deep learning has created a revolution in visually-tracking animal movements (you can build your own [polar bear tracker](https://docs.microsoft.com/learn/modules/build-ml-model-with-azure-stream-analytics/?WT.mc_id=academic-15963-cxa) here), classic ML still has a place in this task.
+While deep learning has created a revolution in visually tracking animal movements (you can build your own [polar bear tracker](https://docs.microsoft.com/learn/modules/build-ml-model-with-azure-stream-analytics/?WT.mc_id=academic-15963-cxa) here), classic ML still has a place in this task.
 
-Sensors to track movements of farm animals and IoT makes use of this type of visual processing, but more basic ML techniques are useful to preprocess data. For example, in this paper, sheep postures were monitored and analyzed using various classifier algorithms. You might recognize the ROC curve on page 335.
+Sensors to track movements of farm animals and IoT make use of this type of visual processing, but more basic ML techniques are useful to preprocess data. For example, in this paper, sheep postures were monitored and analyzed using various classifier algorithms. You might recognize the ROC curve on page 335.
 
 https://druckhaus-hofmann.de/gallery/31-wj-feb-2020.pdf
 
@@ -122,7 +122,7 @@ https://investments.metlife.com/content/dam/metlifecom/us/investments/insights/r
 
 ## 🎨 Arts, Culture, and Literature
 
-In the arts, for example in journalism, there are many interesting problems. Detecting fake news is huge problem as it has been proven to influence the opinion of people and even to topple democracies. Museums can also benefit from using ML in everything from finding links between artifacts to resource planning.
+In the arts, for example in journalism, there are many interesting problems. Detecting fake news is a huge problem as it has been proven to influence the opinion of people and even to topple democracies. Museums can also benefit from using ML in everything from finding links between artifacts to resource planning.
 
 ### Fake news detection
 
@@ -136,7 +136,7 @@ This article shows how combining different ML domains can produce interesting re
 
 Museums are at the cusp of an AI revolution in which cataloging and digitizing collections and finding links between artifacts is becoming easier as technology advances. Projects such as [In Codice Ratio](https://www.sciencedirect.com/science/article/abs/pii/S0306457321001035#:~:text=1.,studies%20over%20large%20historical%20sources.) are helping unlock the mysteries of inaccessible collections such as the Vatican Archives. But, the business aspect of museums benefits from ML models as well.
 
-For example, the Art Institute of Chicago built models to predict what audiences are interested in and when they will attend expositions. The goals is to create individualized and optimized visitor experiences each time the user visit the museum. "During fiscal 2017, the model predicted attendance and admissions within 1 percent of accuracy, says Andrew Simnick, senior vice president at the Art Institute."
+For example, the Art Institute of Chicago built models to predict what audiences are interested in and when they will attend expositions. The goal is to create individualized and optimized visitor experiences each time the user visits the museum. "During fiscal 2017, the model predicted attendance and admissions within 1 percent of accuracy, says Andrew Simnick, senior vice president at the Art Institute."
 
 https://www.chicagobusiness.com/article/20180518/ISSUE01/180519840/art-institute-of-chicago-uses-data-to-make-exhibit-choices