diff --git a/Introduction/3-fairness/README.md b/Introduction/3-fairness/README.md
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--- a/Introduction/3-fairness/README.md
+++ b/Introduction/3-fairness/README.md
@@ -1,7 +1,7 @@
 # Fairness in Machine Learning 
  
-![Fairness in Machine Learning](../../sketchnotes/ml-fairness.png)
-> Sketchnote by [Tomomi Imura](https://www.twitter.com/girliemac)
+![Summary of Fairness in Machine Learning in a sketchnote](../../sketchnotes/ml-fairness.png)
+> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
 
 ## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/5/)
  
diff --git a/Regression/README.md b/Regression/README.md
index 8b124aafc80a3247136d7b68ddd0b30349e13b12..72986e223099d83c8203bd79fd02038598627561 100644
--- a/Regression/README.md
+++ b/Regression/README.md
@@ -1,7 +1,8 @@
 # Regression Models for Machine Learning
 
-![Summary of the lessons](../sketchnotes/ml-regression.png)
-> Sketchnote by [Tomomi Imura](https://www.twitter.com/girliemac)
+![Summary of regressions in a sketchnote](../sketchnotes/ml-regression.png)
+> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
 ## Regional topic: Regression models for pumpkin prices in North America
 
 In North America, pumpkins are often carved into scary faces for Halloween. Let's discover more about these fascinating vegetables!
diff --git a/TimeSeries/README.md b/TimeSeries/README.md
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--- a/TimeSeries/README.md
+++ b/TimeSeries/README.md
@@ -2,6 +2,9 @@
 
 # Time Series Forecasting
 
+![Summary of TIme series in a sketchnote](../sketchnotes/ml-timeseries.png)
+> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
 In these two lessons, you will be introduced to Time Series Forecasting, a somewhat lesser known area of Machine Learning that is nevertheless extremely valuable for industry and business applications, among other fields. While neural networks can be used to enhance the utility of these models, we will study them in the context of classical machine learning as models help predict future performance based on the past.
 
 Our regional focus is electrical usage in the world, an interesting dataset to learn about forecasting future power usage based on patterns of past load. You can see how this kind of forecasting can be extremely helpful in a business environment.
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