diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md
index 62697103..a2a938a7 100644
--- a/2-Regression/4-Logistic/README.md
+++ b/2-Regression/4-Logistic/README.md
@@ -140,7 +140,7 @@ Now that we have an idea of the relationship between the binary categories of co
 
 > **🧮 Show Me The Math**
 >
-> Remember how linear regression often used ordinary least squares to arrive at a value? Logistic regression relies on the concept of 'maximum likelihood' using [sigmoid functions](https://wikipedia.org/wiki/Sigmoid_function). A 'Sigmoid Function' on a plot looks like an 'S' shape. It takes a value and maps it to somewhere between 0 and 1. Its curve is also called a 'logistic curve'. Its formula looks like thus:
+> Remember how linear regression often used ordinary least squares to arrive at a value? Logistic regression relies on the concept of 'maximum likelihood' using [sigmoid functions](https://wikipedia.org/wiki/Sigmoid_function). A 'Sigmoid Function' on a plot looks like an 'S' shape. It takes a value and maps it to somewhere between 0 and 1. Its curve is also called a 'logistic curve'. Its formula looks like this:
 >
 > ![logistic function](images/sigmoid.png)
 >
diff --git a/3-Web-App/1-Web-App/README.md b/3-Web-App/1-Web-App/README.md
index c4b406fa..2e409e78 100644
--- a/3-Web-App/1-Web-App/README.md
+++ b/3-Web-App/1-Web-App/README.md
@@ -199,7 +199,7 @@ Now you can build a Flask app to call your model and return similar results, but
     2. Create **index.html** in _templates_ directory.
     3. Create **styles.css** in _static/css_ directory.
 
-1. Build out the _styles.css__ file with a few styles:
+1. Build out the _styles.css_ file with a few styles:
 
     ```css
     body {
diff --git a/4-Classification/README.md b/4-Classification/README.md
index f6133aa1..73d83beb 100644
--- a/4-Classification/README.md
+++ b/4-Classification/README.md
@@ -8,7 +8,7 @@ In Asia and India, food traditions are extremely diverse, and very delicious! Le
   
 ## What you will learn
 
-In this section, you will build on the skills you learned in the first part of this curriculum all about regressionn to learn about other classifiers you can use that will help you learn about your data.
+In this section, you will build on the skills you learned in the first part of this curriculum all about regression to learn about other classifiers you can use that will help you learn about your data.
 
 > There are useful low-code tools that can help you learn about working with classification models. Try [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa)