With IBM Watson Studio, it is a pretty straightforward process to create a Python, Scala, or R notebook. These notebooks can then be used to analyze, clean, and transform data, and perform numerical simulations, statistical modeling, data visualization, machine learning, and other tasks.

To get us going with this chapter's example projects, we need to take the following steps to create a new project and add a notebook in IBM Watson Studio:

1. Create a new project by first clicking on New project; then, from the Create a project page (shown as follows), find Deep Learning and then click on Create Project: 

2. Next, select a region for the machine learning service to run in then click on Select:

3. Name your project on the New project page (shown as follows), and then click on Create:

4. Now that we have created a machine learning project, we are ready to create a notebook. Notebooks are considered a project asset that can be used and shared. To create a notebook from within the project, you click on Add to project:

5. Once you have clicked Add to project, you need to choose an asset type. In previous examples, we selected the DATA and DASHBOARD asset types; here we will choose NOTEBOOK:

6. Like creating projects, once you select NOTEBOOK as the asset type, you need to provide a name and description under the Name and Description options for the new notebook:

7. In addition, in the bottom left-hand of the page, you will need to select a language for the notebook to use. Notice that the default is Python 3.5:

8. Since we will use Python in our examples, we can simply click on Create Notebook. Take note that IBM Watson Studio notebooks (currently) support the following runtime languages. Once you click on Create Notebook, the notebook instance will be created and initialized for use: