‫Okay.
‫I hope you're starting to get excited as I am about Jupiter Notebooks because I seriously love these.
‫Now, the beautiful thing, the final last beautiful thing about these is that because once you finish
‫your project, you probably want to share your work.
‫Maybe that's through GitHub or give it to one of your teammates so they can check it out for you.
‫Now, Jupyter Notebooks makes that a breeze.
‫What you might have seen is throughout past few lectures is auto saved indicator here.
‫But if we wanted to generate that normally, if we wanted to save our work, so we're finished for the
‫day.
‫And when we want to save it, shut up, shop and come back.
‫Get ready for tomorrow.
‫We can go save here now it's going to automatically save it.
‫Or remember how we're not getting used to using this.
‫If we are in command mode, which is the blue square and no pencil up here, we can hit TSS, which
‫is a shortcut for save.
‫Otherwise, at any time that you want, you can also hit command s if you're on a mac or control s if
‫you're on windows or something like that.
‫It's a good habit to get into if you're continually hitting command s or save or just clicking this.
‫But again, remember, you want to focus on using the keyboard because saving your work often will prevent
‫you having to redo things if for some reason you end up losing something.
‫Okay.
‫So our notebook is saved.
‫Now, how do we shut it down if we come back to.
‫The Jupiter dashboard.
‫We can see running.
‫Now, you've got this shut down button here, but usually you probably won't use that.
‫So to shut our notebook down, we're going to go into terminal and we can see here that we've saved
‫our notebooks several times and this is timestamps.
‫So these are the last three saves that we did pretty close together after each other to shut the notebook
‫down and stop your terminal from running.
‫Going to hit control plus C, then it's going to ask you yes or no and you're going to have to hit y
‫or n within 5 seconds.
‫I hit y because I want to shut it down.
‫And that's what you'll see here, shutting down one kernel.
‫Let's go back here.
‫Go files running.
‫We have to refresh this or close this.
‫Ah.
‫Sometimes a Jupiter dashboard won't update.
‫Because we've closed the connection in our terminal.
‫But that's fine.
‫We can x out of this.
‫So now our Jupyter Notebook is no longer running.
‫It is saved in our sample project folder.
‫We come back here.
‫Normal course.
‫Sample project.
‫There we go.
‫Example notebook and it's going to say date modified today at 11:27 a.m..
‫There we go.
‫Saved it 2 minutes ago.
‫Beautiful.
‫And what you'll see here is we're now back.
‫We've got the environment activated within the sample project folder.
‫So what if we wanted to completely get out of the environment as well?
‫Like to deactivate?
‫Maybe you want to activate another environment to work on another project.
‫So to get out of your current environment, you can type in conduct, deactivate.
‫That's going to take you back to base, the base, the default environment, but we're still within
‫the sample project folder.
‫So if we came back tomorrow, let's let's exit out of everything that we have open, actually, so we
‫can see what it's like to to come back completely from scratch, because this is what what you want
‫to be doing.
‫Remember, this is what we've set up and we've got our project folder.
‫You've seen some data being imported into the workspaces, Jupyter Notebook, and you've seen a little
‫bit a little highlight of using some tools within your environment.
‫So let's reactivate this whole set up again terminal.
‫We're going to increase the size.
‫See how I have bass activated by default.
‫I'm going to change into my sample project folder.
‫So desktop mine stored on my desktop under ML course yours might be different.
‫Remember now I'm within the sample project folder.
‫If I hit LS I can see the image that we uploaded.
‫I can see my environment, I can see my notebook and I can see the data there.
‫Heart disease dot csv.
‫So if we wanted to access a notebook, let's enter Jupyter Notebook.
‫Ah, command not found.
‫Take a second to think about why that command might not work.
‫If you'd guess that we didn't have our environment activated.
‫That's correct.
‫But that's alright if you didn't, because it took me ages to get used to this kind of workflow.
‫But I want to show you it so konda activate not activation to activate we're going to reactivate our
‫environment uses is it uses Daniel desktop slash ML course slash sample project slash EMF.
‫There we go.
‫We notice that base has changed to this.
‫Now, if we run the Jupyter Notebook command again, Jupyter Notebook.
‫What do you think this will do?
‫Let's see.
‫This is going to bring us back to the Jupiter dashboard.
‫Now, if we go into running, we've got no notebooks, no terminals running.
‫We notice that our terminal has changed again to this this funky looking code.
‫All it's saying is we've got a Jupyter Notebook server running on local host .888, which is the same
‫as here Cat eight, not not 888.
‫And we have our notebook here to start it up.
‫You can click on it.
‫And we come back.
‫Beautiful.
‫We've got everything that we were we were looking at before in the previous video.
‫Now, the thing to remember is that when you reload a Jupyter Notebook, all the cells, all the memory
‫of the cells will start from scratch.
‫So we wanted to rerun this cell, remember to run a cell, this shift, plus enter or this run button.
‫But we're ignoring the toolbar we want to get proficient at using the keyboard shift enter.
‫It's going to start back at one and if we do the same two, same three.
‫Now if we ran this a what do you think a will be equal to as the output?
‫Because we've gone to three, a equals six equals five shift enter.
‫I equals six, remember?
‫Because Jupyter notebook cells are run in order of execution.
‫And so this is the workflow that you would take if you were coming back to work on your project again,
‫you might have to rerun all of your cells.
‫So the code say we were working down here, you may have to rerun all of the cells above so that you
‫can work on your code down here and you're not missing out on any data.
‫And the final beautiful thing is because we've now got a workspace that we can work on in entire projects,
‫we can also share this workspace.
‫So if we come back, our sample project folder that we've been working through has our environment,
‫has our data, has this image that we've we've been looking at and it has our notebook which contains
‫code and text about the project we're working on.
‫So if my teammate was like, Hey, Danielle, can you share with me the Jupyter Notebook you've been
‫working on for the Heart Disease Project?
‫I could be like, sure, I could send them this and then they could if they've got this exact same set
‫up on their computer, they could start up an environment, import the data, work through the six step
‫ML framework, and then run the code and read my descriptions throughout my Jupyter Notebook.
‫So that is a beautiful thing about Jupyter Notebooks.
‫They're kind of like the all in one workspace for data science and machine learning projects.
‫So in the next few lectures or the next few lessons and projects, we're going to really dig into this
‫this little framework here at the same time working out how to apply different data science and machine
‫learning tools such as these within our Jupyter Notebook.