diff --git a/6-NLP/1-Introduction-to-NLP/README.md b/6-NLP/1-Introduction-to-NLP/README.md
index af89e1ab..0d47a1d7 100644
--- a/6-NLP/1-Introduction-to-NLP/README.md
+++ b/6-NLP/1-Introduction-to-NLP/README.md
@@ -1,6 +1,6 @@
 # Introduction to natural language processing
 
-This lesson covers a brief history and important concepts of *computational linguistics* focusing on *natural language processing*.
+This lesson covers a brief history and important concepts of *natural language processing*, a subfield of *computational linguistics*.
 
 ## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/31/)
 
diff --git a/8-Reinforcement/1-QLearning/README.md b/8-Reinforcement/1-QLearning/README.md
index d0c13ce7..86819de0 100644
--- a/8-Reinforcement/1-QLearning/README.md
+++ b/8-Reinforcement/1-QLearning/README.md
@@ -1,5 +1,8 @@
 # Introduction to Reinforcement Learning and Q-Learning
 
+![Summary of reinforcement in machine learning in a sketchnote](../../sketchnotes/ml-reinforcement.png)
+> Sketchnote by [Tomomi Imura](https://www.twitter.com/girlie_mac)
+
 Reinforcement learning involves three important concepts: the agent, some states, and a set of actions per state. By executing an action in a specified state, the agent is scored with a reward. Again imagine the computer game Super Mario. You are Mario, you are in a game level, standing next to a cliff edge. Above you is a coin. You being Mario, in a game level, at a specific position ... that's your state. Moving one step to the right (an action) will take you over the edge, that would give you a low numerical score. However, pressing the jump button, you would score a point and you would be alive. That's a positive outcome and that should award you a positive numerical score.
 
 The point of all this is that by using reinforcement learning and a simulator (the game), you can learn how to play the game to maximize the reward which is staying alive and scoring as many points as possible.
diff --git a/README.md b/README.md
index 81fde52d..682b30e9 100644
--- a/README.md
+++ b/README.md
@@ -34,7 +34,7 @@ Travel with us around the world as we apply these classic techniques to data fro
 - Take the post-lecture quiz
 - Complete the challenge
 - Complete the assignment
-- After completing a lesson group, visit the [Discussion board](/discussions) and "learn out loud" by filling out the appropriate PAT rubric. A 'PAT' is a Progress Assignment Tool that is a rubric you fill out to further your learning. You can also react to other PATs so we can learn together.
+- After completing a lesson group, visit the [Discussion board](/discussions) and "learn out loud" by filling out the appropriate PAT rubric. A 'PAT' is a Progress Assessment Tool that is a rubric you fill out to further your learning. You can also react to other PATs so we can learn together.
 
 > For further study, we recommend following these [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa) modules and learning paths.
 
diff --git a/sketchnotes/ml-reinforcement.png b/sketchnotes/ml-reinforcement.png
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