diff --git a/3-Web-App/1-Web-App/README.md b/3-Web-App/1-Web-App/README.md
index f848d807..6150aece 100644
--- a/3-Web-App/1-Web-App/README.md
+++ b/3-Web-App/1-Web-App/README.md
@@ -167,7 +167,7 @@ Now you can build a Flask app to call your model and return similar results, but
         css/
         templates/
     notebook.ipynb
-    ufo-model.pk1
+    ufo-model.pkl
     ``` 
 
    ✅ Refer to the solution folder for a view of the finished app
diff --git a/4-Classification/4-Applied/README.md b/4-Classification/4-Applied/README.md
index 0e20f6c3..773271a1 100644
--- a/4-Classification/4-Applied/README.md
+++ b/4-Classification/4-Applied/README.md
@@ -31,7 +31,7 @@ First, train a classification model using the cleaned cuisines dataset we used.
 1. Start by importing useful libraries:
 
     ```python
-    pip install skl2onnx
+    !pip install skl2onnx
     import pandas as pd 
     ```
 
diff --git a/5-Clustering/1-Visualize/README.md b/5-Clustering/1-Visualize/README.md
index 12ac7c7b..8453c451 100644
--- a/5-Clustering/1-Visualize/README.md
+++ b/5-Clustering/1-Visualize/README.md
@@ -104,7 +104,7 @@ Clustering as a technique is greatly aided by proper visualization, so let's get
 1. Import the `Seaborn` package for good data visualization.
 
     ```python
-    pip install seaborn
+    !pip install seaborn
     ```
 
 1. Append the song data from _nigerian-songs.csv_. Load up a dataframe with some data about the songs. Get ready to explore this data by importing the libraries and dumping out the data: