diff --git a/units/en/unit1/hands-on.mdx b/units/en/unit1/hands-on.mdx
index 419aefd..d3645de 100644
--- a/units/en/unit1/hands-on.mdx
+++ b/units/en/unit1/hands-on.mdx
@@ -34,6 +34,7 @@ You can either do this hands-on by reading the notebook or following it with the
 
 <Youtube id="CsuIANBnSq8" />
 
+
 # Unit 1: Train your first Deep Reinforcement Learning Agent 🤖
 
 <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit1/thumbnail.jpg" alt="Unit 1 thumbnail" width="100%">
@@ -42,9 +43,6 @@ In this notebook, you'll train your **first Deep Reinforcement Learning agent**
 
 ⬇️ Here is an example of what **you will achieve in just a couple of minutes.** ⬇️
 
-
-
-
 ```python
 %%html
 <video controls autoplay><source src="https://huggingface.co/ThomasSimonini/ppo-LunarLander-v2/resolve/main/replay.mp4" type="video/mp4"></video>
@@ -71,7 +69,7 @@ At the end of the notebook, you will:
 
 
 
-## This notebook is from Deep Reinforcement Learning Course
+## This hands-on is from Deep Reinforcement Learning Course
 <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/deep-rl-course-illustration.jpg" alt="Deep RL Course illustration"/>
 
 In this free course, you will:
@@ -90,7 +88,7 @@ The best way to keep in touch and ask questions is to join our discord server to
 ## Prerequisites 🏗️
 Before diving into the notebook, you need to:
 
-🔲 📝 **Done Unit 0** that gives you all the **information about the course and help you to onboard** 🤗
+🔲 📝 **Read Unit 0** that gives you all the **information about the course and help you to onboard** 🤗
 
 🔲 📚 **Develop an understanding of the foundations of Reinforcement learning** (MC, TD, Rewards hypothesis...) by doing Unit 1