‫Welcome back.
‫I'm so excited for this section Y because we're going to start getting hands on with one of my favorite
‫tools in all of machine learning and data science.
‫Now, in the past couple of sections, we've gone through this kind of workflow.
‫We've got your computer.
‫We set up a project folder.
‫We create an environment with some machine learning and data science tools using Conda.
‫Now we're going to start diving deeper into each one of these tools.
‫And more specifically in this section, we're going to be focused on Jupyter Notebooks.
‫Now, if this diagram, this arrow pointing here, these arrows going here, you're like, What's going
‫on here, Daniel?
‫Don't worry.
‫We're going to have a look at this over the next few sections.
‫But what we're focused on for the moment are Jupyter Notebooks, which is kind of like the workspace
‫of where where you work through all of your machine learning and data science projects.
‫And now this is the workflow we're going to be using.
‫You'll have a project folder which we've already created.
‫We'll have an environment, so we'll need to make sure we activate our environment so we have access
‫to these tools.
‫But then once we've kind of got our heads around Jupyter Notebooks and these other ones, this is what
‫it'll look like in practice.
‫You'll have some data, you'll import it into your Jupyter notebook, and then you'll use a bunch of
‫these tools within your environment to derive insights and gain information out of that data.
‫That's enough of the overview.
‫For now, let's get hands on and work with a Jupyter Notebook.
‫So what I'm going to do is open up terminal.
‫If you're on windows, you may want to open up.
‫I'm going to clear that.
‫So we've got a clean workspace.
‫If you're on a windows, you might want to open up the anaconda prompt.
‫And now remember how I said we're going to have to activate our environment to have access to these
‫tools.
‫That's what we'll do first.
‫But actually, we're going to change into our project folder now for me.
‫I'm going to change to my desktop.
‫And I've got all of my information, all of the project folders that I'm working on in the normal course
‫folder.
‫So we're going to go here.
‫Mine's called Sample Project.
‫Yours might be different, but I'll just show you what's in this using LRS.
‫Remember CD stands for Change Directory.
‫So now I'm in my sample project folder and inside the sample project folder is a folder called env.
‫Just to reemphasize where we are.
‫Let's go.
‫I'll go into finder.
‫This might be folder navigator on windows or something like that.
‫So I mean ml course.
‫And if I go over here, I'm in sample project.
‫There we go.
‫We've got EMF.
‫Now, what I'm going to have to do is activate this environment.
‫So I have access to all of my machine learning tools.
‫So let's do that.
‫Conda activate.
‫I'm going to have to type in the file path.
‫Yours may be different to mine depending on where your environment is stored, but that's what we covered
‫in the last lecture is setting up an environment and activating it.
‫So you want to go ahead and activate that environment again.
‫Desktop ML course sample project env.
‫So if I press enter here, you'll notice that it changes from base to having this path name activated.
‫So now my environment is active.
‫I should be able to start up a Jupyter Notebook.
‫We made a mistake notebook by typing in the command Jupyter Notebook and hitting enter.
‫What this is going to do is automatically load up your browser.
‫Your default browser is going to come to the Jupyter dashboard.
‫Very simple here.
‫Again, this is just another view of of what we can see in the sample project folder.
‫So here we have our end folder.
‫If we looked inside that, if we go back into finder, we look inside env, we've got the exact same
‫folders here.
‫This is where we're working.
‫Now.
‫What should you pay attention to on this?
‫First, we'll go back to the home section.
‫We look here, this is showing the files that we've currently got in this folder.
‫Remember, we're in this sample project folder.
‫Then if you come over here, we've got a couple of buttons, upload and new.
‫We'll have a look at those in a second.
‫And you've got two other tabs here running.
‫We'll show you any terminals that you have running in your Jupiter interface.
‫Notebooks will show you any notebooks that you have running.
‫And because we have neither of these running at the moment, it says there are no terminals running.
‫There are no notebooks running.
‫Now, clusters you don't really have to worry about for now.
‫I've been working on machine learning projects for the last couple of years and have never looked at
‫the clusters tab.
‫So we'll come back here.
‫We'll focus on on what really matters.
‫So what are these upload and new buttons?
‫Well, let's have a look.
‫We click upload.
‫What happens?
‫This is going to open up your folder interface.
‫Now, what if we did want to upload something?
‫For example, I've got a heart disease CSV document here for the sake of this lecture, but you're you
‫might have another type of data folder here.
‫So what we can do is if we wanted to, we can open that and this is going to upload it to the Jupyter
‫dashboard.
‫But in reality, all it's actually doing is if we come back to sample project where we were before,
‫now we can see we've got env env and heart disease and heart disease.
‫So realistically this upload button is just like another form of of transfer one folder to another region.
‫So we can do that again.
‫Let's try an image this time.
‫And now this upload will work with any folder or any file that you want to try out.
‫So try it out on your own if you want a link to these resources that I'm using will be available in
‫the resources section.
‫So we'll upload this image.
‫Click there.
‫And so now if we go back to Finder.
‫Go to where we were.
‫Sample project.
‫So we can see now we've got the exact same thing in here.
‫MV six Step ML Framework PNG and Heart Disease.
‫CSV There's one more button we should pay attention to and that's the new.
‫We click on new and this is the most exciting part.
‫We can start a notebook now.
‫It says Python three because when when we click on this, it's going to create a new notebook and the
‫notebook will be able to run Python three code, which is what, what we want.
‫Right?
‫We want to be writing Python code.
‫We want to be writing machine learning code.
‫You can also create text files, create another folder if you wanted to, or create a terminal window.
‫We want to have a look at those now because we're focused on notebooks, which is where we'll spend
‫the majority of our time.
‫So we click here.
‫This will open up a new window.
‫And here we go.
‫The most beautiful thing in all of data science and machine learning.
‫Well, maybe aside from an algorithm that's working really well, is the Jupyter Notebook.
‫And so, again, this interface might look a bit confusing if you've never seen it before, but don't
‫worry, we're going to go through it to start off up here.
‫Is the title of your notebook, so maybe we call it Example Notebook.
‫This will rename your file and if we click this little save icon, it's going to going to click say
‫that it's auto saved here.
‫But really we, we click saved.
‫But that's alright.
‫We come back here.
‫What do we notice?
‫We've got example notebook IP and B, so you see what I mean?
‫If you type in here example notebook, let's just change this for demonstration.
‫Example notebook, one rename.
‫That's going to change this name.
‫And so if we come back to our sample project folder.
‫You'll notice we now have.
‫Example notebook IP NB Now what does IP and B stand for?
‫Well, I python notebook, which is what Jupyter used to be called, but they've kept the file extension
‫as this so that older versions of notebooks don't break.
‫So wherever you see this extension, some named IP and B, this is actually a Jupyter Notebook.
‫Now, if we check the running tab, we can see that we now have a notebook running.
‫We don't have to pay too much attention to this shut down button from from this running tab, because
‫we can we can shut down our notebook elsewhere.
‫But we'll look at that in a future video.
‫The next thing to think about, let's rename this back to example notebook is what is actually going
‫on here.
‫Now as a quick overview, remember, this is the title.
‫This is going to be the menu bar, which is similar to what you may have seen in other applications
‫where you've got file edit, you've got a few different controls here.
‫This is the toolbar, right?
‫So these are commands that you can use on different cells and this is a cell.
‫The most important thing to remember about this is that this cell can contain code or markdown content
‫can contain other things as well.
‫But the two main ones you'll be worried about are code.
‫And markdown code was what allows you to write Python code in here and markdown allows you to write
‫formatted text.
‫So if we wanted to write Hello World, we're going to press shift and enter and there we go.
‫We've got we've got some text there.
‫All right, save this lecture from getting too long.
‫We're going to have a look at different things that we can do with cells, different things that we
‫can do with these these little tools here in the next video.
‫But the takeaway from this is that you can start a Jupyter Notebook once you've got your environment
‫activated, if your terminal window changes to something like this after you run the command.
‫Jupyter Notebook That is completely normal.
‫What this is saying is basically a Jupyter Notebook is running at the local host, which is on your
‫computer.
‫You can see here local host.
‫So it just means that a document is running in your browser.
‫So keep this as it is.
‫We'll return back in the next video to look at more controls and things we can do within Jupyter Notebooks.