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Welcome back.

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In the last video we went to the mini Conda home page or downloads page.

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Remember this is for windows and we downloaded the Windows version Python three point seven of many

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Conda and installed it to our computer.

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More specifically this is what we did download ring counter installed to your computer.

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Now for this lecture we're going to take care of these steps.

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Test the installation create a project folder create a custom environment within activate your new environment

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install Jupiter low to Jupiter notebook.

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Now again I realize you might be seeing in Daniel this a whole lot of steps.

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I just want to get into coding and you're right.

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I'm the same with these sort of things.

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But trust me setting up this workflow is what we're doing.

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You know your computer going to create a project folder and then build an environment using Conda an

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environment which contains all of our machine learning and data science tools.

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It's gonna be foundational and Paramount for future projects going forward.

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So let's do it.

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Now we've gone through the download steps of many conduct and installed it to a computer we can check

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to see if it's there by typing in search and looking for something called an icon to prompt many Conda.

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This will be here once you've installed many condo.

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So we're going to click on that.

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There we go.

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And then what you're seeing here is this Anaconda.

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From what it's referred to in Mac OS Is it terminal.

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But it's essentially a little part of your computer a little program where you can type in code and

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have your computer execute it.

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So what this means is this little section here is called the M prompt.

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Remember we're setting up an environment base is the default environment that gets installed automatically

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when you download mini Conda and subsequently conduct this here is the file path that we're in.

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So I'm on the C drive in the users folder in the user's folder.

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Daniel yours might be slightly different depending on what your name is.

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So now we've got this setup.

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First things first is that we need to create a project folder so let's do that.

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To do so we're going to have a look at what current folders exist in the folder.

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Daniel and we can do that in Windows by pressing D I R which stands for directory and that's going to

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list out a series of folders.

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And if we wanted to see these you might be seeing this is the first time and thinking oh my goodness

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this is this is a bit confusing but let me show you what exactly it's printing out we go into the file

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browser here and go to P.S..

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Now we want to copy this to c c users.

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Daniel.

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That's what we're going to look for.

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I'm going to go to the local DSC users because member we have users here.

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Now we're going to our users Daniel so if we actually type this out C slash users slash.

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Daniel it's the same as what we can see over here.

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So now if we can pare this list here and we have 3D objects contacts desktop documents downloads etc.

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What we want to do is for this use case we're going to create it in the we'll create it on the desktop.

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So we have easy access to what I mean by this is going to create this project folder which is just another

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one of these which is going to contain all of our project information on the desktop.

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So we have easy access to it.

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So let's change to the desktop.

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What I mean by that is see how this path is using slash Daniel to change to the desktop.

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I'm going to press see the desktop.

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What CDE stands for is change directory to the desktop.

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So now if I go the IRR which is going to list all the folders on the desktop it's going to show me sample

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projects if we go on desktop.

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You've got sample project.

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Now we're gonna be creating another one of these.

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So you might not have this already but this is what we're gonna be creating we'll call it sample project

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one for this lecture.

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I've already got a demonstration here that will create a new one.

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So to create a folder we can type the command m k d IRR which stands for make directory because we're

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going to call it sample project 1.

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That's what we tell us.

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So we're telling our computer hey we're in this drive we're in the users folder or in Daniel we're on

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the desktop and I want to make a directory called sample project one going to hit enter.

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Now you can see that is created sample project on we'll go back here.

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So what have we just done.

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Well we've done the first step.

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We've created the project folder.

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So now we're going to use Conda to build this environment.

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If we go back to our flow flowchart create a project folder and change into it.

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So that's what we have to do created the project folder but we're not in it yet.

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So we need to change into it.

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Remember to change directory and press S.D. into sample project 1.

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So now this is what we have we have we're in here.

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We're on the desktop.

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We've got sample project 1.

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And if we go desire to see what files are in here there's actually nothing in here so zero bytes zero

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files.

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If we look sample project 1 This folder is empty.

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So that makes sense.

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Okay now what's the next steps changed into it.

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Create a custom environment within the project folder.

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Well let's do that so to use Conda to create an environment.

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Remember if we look at this we're going to use Conda to create this.

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So that's this step you can run the command conduct.

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It's like saying hey conduct our personal assistant create.

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We're gonna pass it the prefix tag.

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Now I'm gonna write this and slash and hand as Num pi map but lib and socket line you might be looking

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at this going my goodness Daniel what what is going on here.

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Well that's a great question.

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So remember what we're saying is conduct.

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Hey personal assistant conduct create.

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Actually that was a bit that was with me and not yelling that pretend this is saying Please conduct

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create prefix when you see these double hashtags it's like a little command that can go to the create

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function.

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This dot means it's going to use this file path here.

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So essentially saying the DOT saying within the sample project one folder and create a folder called

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M and within that end and this shortfall environment which is this within that end install panders num

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pi map plot lib and socket learn.

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Now what are these.

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Well if we look here at what lib num pi panders socket line.

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So let's run this by pressing enter and see what happens

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so now our personal assistant conduct what just happened.

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A whole bunch of code just appeared.

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Because we've we've ran this command here.

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Conduct create prefix and etc. What this is going to do is conduct our personal assistant for collecting

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data science tools.

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Remember these are data science tools.

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Here is going to go to the Internet and download or query the Internet and go Hey Daniel wants to create

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an environment on his computer with map plot Labor num pi pandas and psychic line.

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And so the following packages will be downloaded.

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This is a message from Canada.

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All we did was press enter what Canada is going to do is because these packages themselves or these

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tools require some other tools within them to operate.

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So lots of code lots of the reason why is because people have written lots of code in the past to prevent

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rewriting all the code.

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A lot of existing tools leverage another bunch of existing tools so conduct is automatically.

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So this is a beautiful thing about content.

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It's going to download all of the extra tools that we need to get started.

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And this is what this is listing out here so we want to proceed to do so we'll press why now conduct

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is going to download and extract extract means install all of the packages it requires to get set up.

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So now this might take a minute or so or maybe a little bit longer depending on your internet connection

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or hard drive speed.

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So I'll let this run through and we'll resume the lecture once it's finished

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excellent conduct has now finished that took a few that a minute or so on my computer.

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What is actually done is it's gone to the Internet it's downloaded and installed these packages.

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So a big long list of them into our environment and let's have a look over here.

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So this is what it's done.

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Create a project folder.

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Now we've asked Condi using the com to create command to create this environment.

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So if we go back to our sample project one folder there's now a folder in here called in which is remember

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short for environment we go in here by clicking here.

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It's gonna have a whole bunch of different files in here.

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Now to be honest I don't even understand what's going on in here.

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What is important to know is that this contains all of our data science and machine learning tools.

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So now we have a project folder with an environment with all our tools ready to go so to access that

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environment and to access the tools that we want to use.

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Condit tells us to activate this environment.

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Use the command contact activate C users Daniel.

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Desktop sample project 1.

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And now you might want to write this one down.

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If you're doing this for the first time you also might be slightly different to mine.

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Your user name might be different and you might have called this something else because you can call

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this whatever you like.

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You should really call and always call M M.

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That's a universal name for or shortened version of environment to activate this environment that we've

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just created we can use this command and now I'm a big fan of typing out commands.

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So we're gonna go instead of just copying and pasting which we could definitely do but this practice

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typing out these things because we want to.

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After all that's what we want to do we want to start writing our own code and type this out sample project

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one slashed end run.

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Now what you'll notice has changed so I just literally just type this out and press enter.

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If we hit Enter you'll notice this base has now changed to this file path here which means this is the

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environment prompt.

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So now we've activated this environment.

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What that means we'll have access to all of the tools in this environment.

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So the ones we just installed so let's try and test it out to do so we'll try and load a Jupiter notebook

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using the command Jupiter notebook.

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And if you never use a Drupal notebook before don't worry.

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That we a section on that but a Jupiter notebook is what you need to know is kind of like a data scientists

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work pad you can write code you can write.

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You can write text you can work on projects in fact all of the projects we work on will be within DuPont

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notebooks.

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It's like the basis of all of our machine learning work.

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So let's try and start one up.

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Okay.

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We're getting an error here.

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Jupiter is not recognized as an internal or external command.

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Now the reason being is if you remember the exact code that we ran before and it's okay if you don't

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we did Condit create prefix and pandas known pi map Gottlieb psychic line.

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Mm hmm.

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So we go back to our diagram what do we have pandas now what label non pi socket line but no dupe at

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all.

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We forgot to include Jupiter in this line.

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Not to worry Conda our personal assistant is back to the rescue because there's a handy command called

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Connor install what this means is if we type in Jupiter.

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Connor install is going to install a new tool to our current active environment which is exactly what

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we want and since we want to use Jupiter notebooks we need the Jupiter package.

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So let's run that by hitting enter

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Connor is gonna do the same thing as what what it did before we go up here we've seen all these before

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except this time it's only focused on Jupiter in the environment location uses Daniel desktop sample

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project 1.

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So if we follow this through it's gonna install all of these extra packages because Condor is amazing

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it's our personal assistant and all these packages are tools that we need for data scientists and we

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go why to run.

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And again it's gonna go through these download and extracting stamps will pause the lecture wait for

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this to finish and we'll come back once it's done.

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Beautiful.

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Now that that's finished we should have access to Jupiter.

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Let's let's do a quick recap of what we've done.

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So we've created a project folder changed into it.

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That sample project 1 Create a custom environment.

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We've done that with com to create activate our new environment.

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Yeah we've done that with to activate install Jupiter because we tried to access Jupiter but it didn't

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work with Conda installed Jupiter.

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Now we have to load up a Jupiter notebook and check the tools we need.

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OK let's try that.

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So if we've actually installed Jupiter should be able to run the command again Jupiter notebook the

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one we tried before that didn't work.

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We'll type that in Jupiter notebook and hit enter.

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So now what this is going to do is going to automatically open your browser your default 1 and launch

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the Jupiter interface so we can see here a few maybe a few new things here.

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If you've never seen it before what's important to note is that the Jupiter interface has been created

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from within the folder that we're currently in on terminal so uses Daniel desktop sample project 1 Jupiter

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notebook.

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It's like telling your computer Hey started Jupiter notebook within the sample project one folder.

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So we look here we've got the end folder we compare that to our sample project one we've got the end

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folder as well.

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Now to start a Jupiter notebook where the workspace where a data scientist or machine learning engineer

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will do a lot of coding working on different projects go up to here new notebook Python 3.

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And again we're going to have a whole entire section on Jupiter notebooks.

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So don't worry too much if you're not sure what's happening here.

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So what this is going to do once happened here oh there we go.

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They just took a little while to load.

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So what we did there we've actually got to because that took a.

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There was something going on there with my computer and it took a while to start up.

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There we go Untitled I pi and b stand so I Python notebook which was Jupiter's previous name.

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And so this is the Jupiter interface.

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Absolutely beautiful space we're gonna get very familiar with using this throughout the course.

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And again we're gonna have an entire section on this.

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But what you need to know for now is that you can run Python code in here.

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So hello world and then press shift and enter and that'll come through.

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This is this cell in Jupiter notebooks.

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This is referred to as a cell you can also change it to mark down which is like saying text and say

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this cell does simple some and the one below is going to be one plus one shift and into.

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Well what we're focused on doing is testing to see if our environment is set up.

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So specifically we've ticked off Jupiter we've got access to Jupiter because we're in the Jupiter interface.

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Now we want to see if we have access to pandas psychic line none pi and map probably we can do that

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by importing them.

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And remember the import statement is like saying hey Python go and get these packages or tools for us

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and bring them into our workspace so we can access them.

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So do import pandas as PD import num Hai as any import map plot lib dot pie plot no pipeline as BLT

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import as K line which is short for socket learn.

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Now these might be like pandas is paid a want and num PIs MP might apply plot.

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The reason why we shorten it to these things is so we don't have to always type pandas.

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Now if you ever see PD or NDP LPL t you can probably be safe to assume that their pandas name pi met

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properly or pipeline to test these if we actually do have access to them in our environment is set up

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we can run this cell and if there's no errors we should have an environment ready to start doing data

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science and machine learning work.

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Fingers crossed beautiful.

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We can tell it.

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There was no errors because you'll see a number here every time you run a Jupiter cell we'll go over

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this and in a future lecture the number will go up so the first one you run will be one that's seven

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now because we've run seven different cells but this is exciting we've just set up an environment to

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work on machine learning and data science projects let's have a look.

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We started with this your computer and we downloaded mini conduct which came with Python and conduct

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then we used Conda to install these tools specifically within an environment.

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Now if we go back here we've gone through these steps in a previous lecture we downloaded installed

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mini Condor we tested our installation by the anaconda prompt we created a project folder sample project

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one and changed into it created a custom environment within the project folder we activated the new

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environment.

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So we have access to tools we installed Jupiter then we loaded up a Jupiter notebook and check to see

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if we had the tools we needed which we did.

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Well that was a lot to take in.

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Don't worry I will have plenty of practice going through this in future projects and in lectures.

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So what do we do now.

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We've got our Jupiter notebook running.

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But maybe you want to.

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You've finished for the day and you want to close it.

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What I've just done is saved so to save a Jupiter no boat.

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You can either click this Save icon and that'll that'll save it once it loads or you compress command

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air so control s or something like that.

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But to close it down we go back to our Anaconda prompt and we're going to press control see

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so there we go.

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Press controls say now it says shutting down one colonel Colonel shut down the colonel is what runs

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our troop at a notebook in the background.

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So now you'll see here that the connection has failed to our Jupiter nightfall.

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So we'll go okay.

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That means that Jupiter notebook we're no longer connected to it.

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So we're back here and we're still in our environment.

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But to deactivate the environment maybe you wanted to get out and create a new environment.

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So we're gonna type in conduct deactivate what this will do is take us back to base.

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Now why would we want to do that.

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Well maybe we want to create a new environment.

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Maybe you want to work on another project such as sample project 2 and that uses a different environment.

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It might be a plethora of reasons.

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However how do you get back into your environment.

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Well luckily we wrote down that piece of code before if he didn't.

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It's okay.

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It's to conduct activate.

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See we're going to write this this and slash EMP.

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Now this is if you are coming back to your work and wanted to get back into your project.

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Daniel slash because this is where we created our environment sample project 1.

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Now this might be in the future might be a sample might be project to maybe project 3 or something like

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that.

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We usually have and that the end here to conduct activate our environment beautiful now a back from

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base to having our environment active so then we can type in to put a notebook load back up a Jupiter

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notebook is going to take us to the home page.

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There we go.

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We've got our Untitled I Spy and B which was saved two minutes ago.

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Click on that

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and there's our code from before.

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So then we could continue work on our project save it when we need to and exit when we don't need to

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work on it.

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Well we're gonna see this more and more for each single project that we work on.

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What's important to remember is that this is what we've done we've set this up for the future.

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We have our computer.

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We've created a project folder.

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Say we wanted to work on heart disease classification.

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We'd make a folder for that.

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You create an environment install the tools that you need using Conda.

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Then if you're working by yourself you might not need to do this but in the future when you're on when

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you're on teams this is one of the biggest challenges we work through as a machine learning team.

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Sharing these environments so other people could work on the same projects as us.

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So that's what we've done.

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So upcoming lectures and projects we're gonna do the same steps create a project folder build an environment

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with the tools we need using Conda and then every time we're going to go in activate our environment.

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So we have access to the tools and then work on our projects in Jupiter notebooks.

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That was a lot to take in but don't worry there's attached resources.

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And if you want re watch this video go through it step by step.

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Check out the resources section for a step by step guide.

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And if you get stuck Don't worry I've got stuff plenty of times when I was figuring this out.

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It took me a few guys.