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Over the next couple of lessons we're going to talk about Python programming in depth.
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The aim of this section is to make sense and explain the code that we wrote in the previous lessons
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when we were estimating the movie revenue.
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As such, the next lessons are not going to teach you more machine learning concepts.
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Instead, the lessons will focus on the Python programming fundamentals.
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We're going to take a deep dive into the Python code that we wrote for visualizing our data and for
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running our regression in the Jupyter notebook.
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What Python programming fundamentals will we look at?
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We're going to discuss programming concepts such as variables, data types, functions, methods and objects.
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These are the fundamental stepping stones on which all the other future lessons are going to be built.
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For these lessons,
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I will assume no prior programming knowledge.
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So if you've already got some programming experience feel free to skip ahead through the upcoming videos
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or just watch the videos at double speed.
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Now I also tried really really hard to find you guys some interesting data sets for us to look at. And,
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I came across a pretty weird medical study that was done in the United States in 1968.
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So for our programming exercises, we will use the data from this study which examines how math test scores
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were affected by the use of psychedelic drugs.
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Now, without further ado, let's jump straight into how to get set up.
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Let's fire up a new Jupyter notebook.
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This time we're going to use the locally installed version instead of the try.jupiter.org
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web site.
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Now there's different ways you can do this.
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First let me show you how to do it with the Anaconda Navigator.
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So simply open the Anaconda Navigator and then click on "Launch" Jupiter notebook. At this point,
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the Jupyter notebook will open in your browser but you'll notice if you look at your address bar, instead
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of it reading try.jupiter.org,
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it will read localhost. The other way to open a Jupyter notebook, without going through the Anaconda
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Navigator is simply by going through the terminal or the command prompt.
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First, let me show you how to do this on a mac. On a Mac,
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you'd open the terminal
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and then you would type "jupyter notebook" and hit Enter and you'll get exactly the same result as going
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through the Anaconda Navigator.
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Since loading the Anaconda Navigator can be a bit slow at times,
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I actually prefer this as a way of getting to the Jupyter notebook very, very quickly.
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Now, if you're running a Windows machine, you don't have a terminal.
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Instead, you'll have something called the command prompt.
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In fact, when you installed Anaconda, it would have actually given you something called the "Anaconda prompt"
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as a convenient shortcut. You'll find this Anaconda Prompt in your start menu.
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So if I go to show all my apps, it's right here - Anaconda prompt.
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And this is what it looks like. Since I use this Anaconda prompt quite a bit,
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I've actually pinned it to my taskbar for easy access and this is what I'll recommend for you guys as
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well. In the Anaconda Prompt,
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you simply give the same command as on Mac.
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You're right - "jupyter notebook".
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When you hit enter, the Jupyter notebook should fire up in your default browser.
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Now, what we're gonna do is we're going to create a folder where we're gonna save all our work and we're
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gonna call this folder MLProjects. The way to create this folder is to go to New and then go to New
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Folder.
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You should then see an untitled folder appear.
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But instead of leaving it as "Untitled Folder", you're going to put a tick box next to it and then you're
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going to hit "Rename".
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And here you're gonna give it the name MLProjects and confirm, so click on your MLProjects folder
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to open it.
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And then once inside you're going to create a new Python notebook.
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Click on "New", then "Python 3"
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and then let's rename this notebook to "02 Python intro", hit "Rename" and then let's navigate back
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to the MLProjects folder.
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You've probably done some work in the previous lesson when we were working on try.jupyter.org
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and you've probably downloaded your notebook at the time. What you can do now is upload that notebook
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to your MLProjects folder.
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That way you can keep all your coursework together.
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So just click "Upload".
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Select your Python notebook and select the cost_revenue_clean data set as well -
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that CSV file that I provided previously and then click "Open".
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You should see the two line items appear in your MLProjects folder where you can confirm by hitting
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the blue "Upload" button. Now you've got your previous work accessible here together with the data set
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that we used. Great!
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Now we're all set to dive into Python programming.