‫Well everyone and welcome to the zero to Mastery ML GitHub repo.
‫Now if you're looking at this in the video that you're watching and you do visit this URL here, github
‫dot com slash Mr. de berk slash zero two mastery ML.
‫You might notice that this repo has changed and that's because this course is continually being built
‫and continually developed.
‫And so as things get added and changed, the folder structure here, the red means may change, but
‫the work process we're about to follow won't.
‫And now the particular workplace where we're following is throughout the course.
‫You may notice that we use Jupyter Notebooks a whole bunch because Jupyter Notebooks are the workspace
‫for a data scientist or machine learning engineer.
‫And so if you wanted to download everything in this GitHub repo, you could follow the the clone or
‫download a little section here.
‫But maybe you want to work on one particular notebook.
‫You don't want everything, so you just want to access one notebook.
‫Maybe it's part of the exercise or the assignment.
‫Let me show you how you could do that.
‫Let's say we want to do the pandas exercises notebook.
‫We want to work on that.
‫So we'll click on this one.
‫Pandas exercises.
‫We got this URL here.
‫If we wanted to download this one particular folder or this one particular file, we can go click on
‫this raw button.
‫And then what we're going to do is it's going to bring up all this text, but that really doesn't make
‫sense to us.
‫So we'll find a way to to make it make sense.
‫We'll go file save page as and I've got a folder on my desktop called ML course and I'm going to save
‫this into my ML course folder where I'm working on all of the course materials.
‫You may have some sort of folder like this and I'm going to save it into the sample project folder.
‫I'm just going to keep it with that file name.
‫So Save Beautiful that's going to download and I'm going to open up a terminal.
‫Let's see how we'd access that one particular Jupyter Notebook.
‫So what I'm going to do, I'm going to change directory to my desktop and then to my ML course folder
‫and then again to the sample project folder where I just downloaded.
‫If I press LHS, I should have pandas exercises IP, IP NB text, which is this text file here.
‫So how would I access this to work on it?
‫Well, what I'm going to have to do is activate my anaconda environment.
‫So if I go on to activate and if you're not sure what this means, don't worry, we'll cover this as
‫well.
‫I'm just showing you how you access one particular notebook.
‫So another in in a summary Anaconda environment is a collection of tools to work on data science and
‫machine learning projects.
‫So users mine is stored here.
‫Daniel Desktop ML Course.
‫Door sample project slash end.
‫So it's this little file here.
‫This is what I'm trying to activate.
‫Now, hit enter there.
‫You'll notice it changes from base to this file path here.
‫Beautiful.
‫And since I have my environment activated, I'm able to access the Jupyter Notebook tool.
‫So if I hit that.
‫This is going to load up the Jupyter Notebook server running on my computer.
‫And you might notice we've got pandas exercises.
‫This is that file we downloaded just before here.
‫Pandas exercises, dot IP, Y notebook and B is for notebook.
‫And we've got some other ones here that I've got a little different symbol.
‫Right.
‫So this IP and B stands for I Python Notebook or Jupyter Notebook.
‫Now, how would we convert that into one of these?
‫Let's see.
‫We just open this up.
‫An easy way to do it is just by changing removing the dot text extension and clicking okay and then
‫hitting commands.
‫Or actually we don't even need to do that.
‫It's going to update itself automatically.
‫We come back to here.
‫Beautiful.
‫You notice that pandas exercises IP and B has now changed to this little book symbol.
‫So if we close this and reopen this notebook.
‫What we can see is we've got an individual notebook.
‫Downloaded from GitHub.
‫And running on our computer now if we check to see if this works import pandas is PD shift and enter.
‫Wonderful.
‫So that cell is run without error.
‫So we might delete that because we don't need that.
‫Now you might be thinking if you just going through this, you might be thinking, Wow, Daniel, that's
‫a whole bunch of steps.
‫But don't worry, as you work through the course, you will get more and more familiar with that kind
‫of workflow that we just went through.
‫However, if you'd like to get started far quicker, there is another way.
‫So we're going to come back to here.
‫And one way you can do that is by going into.
‫Whichever notebook you want to open.
‫We're going to copy the URL.
‫So in this case, it's just the exact same one as before pandas exercises dot IP and B, and we're going
‫to go to a little tool called CoLab.
‫And now this is made by Google.
‫And so the folks at Google had this problem like we're having now.
‫And Jupyter notebooks, as amazing as they are, can be a little little bit hard to get start to get
‫running and so Google what they've done is they've created a tool called CoLab which is essentially
‫Jupyter Notebooks in Google Drive.
‫So you will need a Google account to do this little workflow.
‫But what you can do is go to this website here and what we can see, we've got my Google drive there,
‫but we've also got GitHub so I can copy this URL of this Pandas practice.
‫Jupyter Notebook, come back to CoLab, go to the GitHub tab and enter the URL there.
‫And if everything's working correctly, Code Lab should automatically take all of this information from
‫the Jupyter Notebook on GitHub.
‫Ah.
‫There we go.
‫I should have pressed enter when I hit that in there.
‫So if we come in here, this is the one we want.
‫So there's a little tidbit for you.
‫Make sure you press enter when you copy the link.
‫And there we got it.
‫So now we've got our Jupyter Notebook from here.
‫In a Google CoLab Jupyter notebook in these run exactly basically the exact same they've got they look
‫a little different to a traditional Jupyter notebook, but this is because they've got Google style.
‫So if we go import, we run that exact same code, import pandas as PD.
‫Warning.
‫This notebook was not authored by Google.
‫This notebook has been loaded from GitHub may request to access your data stored within Google or red
‫dot and credentials.
‫Please review the source code before executing this notebook.
‫So that's something you might have to.
‫If there are any warnings like that, make sure you read through them.
‫But otherwise you can notice that this notebook is working perfectly after just copying the URL from
‫GitHub and typing it into colab research.
‫Google.com.
‫And if you want to open something github, copy the URL, hit enter and then click on the notebook you'd
‫like to open.
‫In this case, we're just open the exact same one.
‫Then.
‫There we go.
‫We can get started running coding nice and easily.
‫So that's just a little tidbit.
‫Remember, if you do refer back to the zero two master e-mail GitHub and it looks a little different
‫to what you've seen in this video, don't worry too much.
‫It's just because as we go, we're updating it and continually adding new and updated content.