Edits suggested for chapter 3

modules/chapter3/pages/index.adoc

@@ -1,6 +1,6 @@
 = Jupyter Notebooks
 
-This chapter introduces you to Jupyter notebooks, why they are considered a useful tool for data science, and how you can use them.
+This chapter introduces you to Jupyter notebooks, explains why they are considered a useful tool for data science, and shows you how to use them.
 
 Chapter goals:
 

modules/chapter3/pages/section1.adoc

@@ -7,12 +7,13 @@ Jupyter notebooks have become a popular tool for data scientists who work in dat
 To fully understand the Jupyter ecosystem, you must understand the following three concepts:
 
 Notebook file::
-A notebook file, also called _Jupyter notebook_, or just _notebook_, is a file that contains code and multimedia content, including text, images, and audio.
-This files typically use the `.ipynb` extension.
+A notebook file, also called as _Jupyter notebook_, or just _notebook_, is a file that contains code and multimedia content, including text, images, and audio.
+This file typically uses the `.ipynb` extension.
 +
 A notebook contains cells.
 These cells can contain either Markdown or executable code.
 
+
 JupyterLab::
 JupyterLab is the execution environment that runs the notebook files.
 It exposes a web interface for you to visualize, edit and execute the notebooks on a web browser.
@@ -25,10 +26,11 @@ It also provided a notebook execution environment and a web interface.
 Note that the naming of the classic Jupyter Notebook environment can lead to confusion, because the _Jupyter notebook_ term can also refer to a notebook file.
 ====
 +
-There are many ways to run an execution environment for notebooks.
-You can install JupyterLab with `pip` and run it on your computer.
-You can use the JupyterLab instances included in the RHOAI workspaces.
-You can use online notebook execution environments, such as Binder, Kaggle, or Google Colab.
+There are many ways to run an execution environment for notebooks. They are as follows :
+
+* You can install JupyterLab with `pip` and run it on your computer.
+* You can use the JupyterLab instances included in the RHOAI workspaces.
+* You can use online notebook execution environments, such as Binder, Kaggle, or Google Colab.
 
 Project Jupyter::
 This is the umbrella open source project that defines the notebook file format and develops and maintains the JupyterLab and the Jupyter Notebook environments.
@@ -55,9 +57,9 @@ These cells contain Markdown content and are intended to add rich text to the no
 Code cells::
 These cells contain executable code.
 JupyterLab includes support for cells that run Python code, but you can add other execution environments.
-These environments are called _kernels_.
+These environments are called as  _kernels_.
 
-The exercise bellow covers cells in more detail.
+The exercise below covers cells in more detail.
 
 == Kernels
 

modules/chapter3/pages/section2.adoc

@@ -59,7 +59,7 @@ You have now created your first notebook.
 +
 [IMPORTANT]
 ====
-Before creating a notebook, verify that your are at the directory where you wish to create the notebook file.
+Before creating a notebook, verify that you are at the directory where you wish to create the notebook file.
 Use the JupyterLab file browser to navigate to the right directory.
 ====
 

modules/chapter3/pages/section3.adoc

@@ -5,7 +5,7 @@
 // Customize notebooks by installing third-party packages
 
 RHOAI provides preconfigured notebook images to create workbenches.
-Each of these images is designed to support your data science workflow in specific and popular technological stacks.
+Each of these image is designed to support your data science workflow in specific and popular technological stacks.
 For example, if you plan to work with PyTorch, then you might want to use the `PyTorch` image.
 
 The notebook image that you pick, however, might not include all the libraries that you need for your project.
@@ -24,7 +24,7 @@ The second option is covered in the https://redhatquickcourses.github.io/rhods-a
 
 RHOAI ships with a collection of notebooks images, which are optimized and supported by Red{nbsp}Hat and independent software vendors (ISVs).
 These images are designed for data scientists, providing them with the necessary tools to start working quickly.
-Moreover, Red{nbsp}Hat provides support for these images for at least one year, and releases major versions of each image typically every six months.
+Moreover, Red{nbsp}Hat provides support for these images for atleast one year, and releases major versions of each image typically every six months.
 
 By default, RHOAI includes these images: