So in this lecture, I want to talk about how to practice what you've learned in this course and how
to make sure you are able to carry out the tasks you've been taught to do.
Now, firstly, as mentioned in the introduction to this course, this is a hands on course.
So what does this mean?
This means that in this course you are strongly encouraged to get your hands dirty.
Yes, you your actual hands typing on your keyboard, the things you learned in these lectures.
So a few of you might be wondering, how does this work?
So let's talk about how this works.
Basically, the lectures in this course will proceed in a two step pattern.
The first step will always be to discuss the concepts.
What's going on?
What are we trying to accomplish, how does it work?
And so on and so forth.
That's the first step.
The second step will be to implement those ideas in actual working code.
This is where getting your hands dirty comes into play.
You see, what I want you to do is when you see these lectures, I want you to open your own a notebook
or your coding environment and start typing out the stuff you see so that you learn how to implement
these concepts yourself.
Too often what I see is students just downloading notebooks and heading play, and what ends up happening
is that they think they know what they're doing, but in practice they are completely incapable of writing
any code themselves.
And if you ask them, Do you know how to write this code, they will say yes.
And then if you ask them, well, how much of this code did you actually write?
The answer is none of it.
And, you know, if you ever get a job or work in a project, that's what you have to do.
You need to write actual code, not just believe in your mind that it can be done.
The real world requires you to code, not to just watch other people code or download code that's already
been written.
The real world doesn't reward you for simply believing in yourself.
The real world rewards you when you produce useful outputs.
So that's what you're learning to do in this course.
Produce useful outputs, not just thinking you can.
Now, this course is mostly an API focused course.
This is opposed to a more theoretical course where you might be taught an algorithm and then be expected
to implement that algorithm from scratch.
For this course being mostly an API focused course, you're not going to know the syntax until they
actually show it to you.
So in that sense, you will need to see the code as you code.
I think this is important to mention since many of my courses are courses where you are taught algorithms
and then you implement those algorithms as an exercise.
This, however, is not one of those courses.
In this course, you will need me to show you the syntax which you will be shown in the lectures.
Now you might be wondering, isn't coding a mental thing?
Why do I actually physically have to code?
Isn't it enough to just see some syntax with my eyes?
And, well, the answer is no.
This has to do with a concept called muscle memory by typing your building muscle memory, which will
help you produce similar code in the future.
Now, for those of you who might be suspicious of this claim, please email me and I'll send you a link
from a famous machine learning professor from Stanford who recently wrote about this.
It is simply undeniable that this is good for your learning experience, and if you don't trust me,
then you can at least trust this Stanford professor who is very well known in the machine learning community.
Now, here's one thing I do not want you to do, which I've seen some students do in the past.
And guys, please don't do this.
It's a very silly.
And also, if you do end up doing this, I'm going to know that you didn't pay close attention to this
lecture.
So what is this big mistake that I don't want you to do?
I don't want you to type any data links you see by hand.
And I don't want you to Google, search the links for the data sets in this course.
So just to repeat that, do not type data links by hand and do not Google search the data links.
Why?
Because the data links are already given in this lecture.
Just click the resources tab or go to the extra section and you will see a link to a document containing
all the data links for this course.
So yes, for the code portions, I do want you to type all of that by hand because that's actually useful.
Typing code is useful.
Typing data URLs is not so useful.
Now, one thing you'll notice while watching the code lectures is that I myself do not type the code
during these lectures.
Of course, that's completely unnecessary.
Some students in the past have made the mistake of thinking that because I wasn't typing that they also
could not type.
Of course, this is very silly.
You don't need me to type in order for you to type.
When you see any code, you can type that code.
This allows me to focus on explaining things to you, which is more useful than just typing on the keyboard.
And guys, just so we're clear, when you see a 20 minute lecture of just typing straight through,
which is what you might find in some other course.
This is not an accurate depiction of how that code was actually written.
For a lot of this kind of code, sometimes it takes hours and maybe even days to finish.
So if you think watching me type a notebook from beginning to end is going to give you some idea of
how it's done live, it won't.
It would simply lead you to a false impression of how to write code in the real world.
Now, one question I get sometimes is that the code in the videos is too small or too blurry to see.
So firstly, please note that this course has been optimised to be viewed on a desktop or laptop computer.
It wasn't really designed to be viewed on, say, your smartphone because you can't simultaneously write
Python code on your smartphone, at least not with today's smartphones.
So that's tip number one.
Make sure you are using a full sized screen and not a smartphone screen.
Tip number two Note that you can change the quality of the video.
For some students with slow Internet connections.
This platform will automatically adjust the quality to have a lower resolution.
The result is that the video looks blurry compared to the original.
The original resolution is 1080 PPI, and you can select this manually from the video player.
And if you don't know how to do this, please contact the technical support team.
And finally, tip number three, make the video full screen.
When you do this, the text actually becomes larger than it was on my own screen.
I've also checked this myself and can confirm that the code you see in the videos is larger than the
font size in the New York Times.
So as long as you're able to view regular websites, then you should be able to view the code in these
lectures.
So aside from just notebooks, there is yet another place to get extra resources for this course.
For example, reading materials, data processing scripts, and other miscellaneous things.
To get these, please go to my GitHub repo GitHub slash lazy programmer slash machine learning examples.
This contains materials for all my courses in a single repo and it's organized by folder.
Essentially it's one folder per course, although there are some exceptions.
In order to go directly to the folder for this course, please click the resources link attached to
this lecture.
Please note that the GitHub repo also contains a file called Extra Reading text, which includes links
to papers, journal articles, tutorials, books and other useful resources for this course.
Within this course I may refer to research papers, in which case links to these research papers can
be found in this file.
Extra Reading Text.
Now if you're a new to Git or GitHub and you're a bit intimidated, please see the upcoming lecture
called How to use GitHub and Extra Coding Tips.