Remove mention of chapters 10-12

chapters/en/chapter1/1.mdx

@@ -23,7 +23,7 @@ Here is a brief overview of the course:
 
 - Chapters 1 to 4 provide an introduction to the main concepts of the 🤗 Transformers library. By the end of this part of the course, you will be familiar with how Transformer models work and will know how to use a model from the [Hugging Face Hub](https://huggingface.co/models), fine-tune it on a dataset, and share your results on the Hub!
 - Chapters 5 to 8 teach the basics of 🤗 Datasets and 🤗 Tokenizers before diving into classic NLP tasks. By the end of this part, you will be able to tackle the most common NLP problems by yourself.
-- Chapters 9 to 12 go beyond NLP, and explore how Transformer models can be used to tackle tasks in speech processing and computer vision. Along the way, you'll learn how to build and share demos of your models, and optimize them for production environments. By the end of this part, you will be ready to apply 🤗 Transformers to (almost) any machine learning problem!
+- Chapter 9 goes beyond NLP to cover how to build and share demos of your models on the 🤗 Hub. By the end of this part, you will be ready to showcase your 🤗 Transformers application to the world!
 
 This course: