쉐도잉 연습: How I Would Learn Python FAST (if I could start over) - 영상으로 영어 말하기 배우기

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Back in 2019, I had exactly two months to master Python

because somehow I got into this cool research project lab that required Python experience,

but I had exactly no experience whatsoever.

So not only did I have to learn Python,

I had to learn it really well, fast.

So in this video, I'm going to show you what I wish I had.

The five level Python pyramid designed to take you from zero experience to advanced level in building real Python projects with AI.

But please remember, you have to follow this pyramid in order to ensure your foundations are solid.

So with that being said,

sit back, relax, take notes, and let's get started.

The first layer of the pyramid is the base, your fundamentals.

Do not start coding right away.

Trust me, you'll be shooting yourself in the foot because you must first learn the art of the language,

the art of Python.

Just like you wouldn't want to wake up one day in France and someone tells you,

Hey, start speaking French.

No, no, no, no. You want to make sure that you actually learn the language and learn it the right way.

So first let's understand Python's syntax.

In the real world, we count things like one, two, three, four, five.

But in Python, we actually go zero, one, two, three, four.

The first number is always zero.

It's a zero index based language.

So instead of saying, hey,

can you give me the first item on the list?

You actually say, hey, get me the item at index zero.

So the next time someone tells you,

oh, you were my first love,

that's actually a red flag,

because who was number zero?

Another thing, say you're going shopping,

you might say drive until you reach the store.

But in Python, we use what we call a while loop.

While you are not at the store, drive.

So until the condition of you being at the store is true,

you shall continue to drive forever.

So it's a bit of a frame of mind difference that I really want you guys to hone in on.

But what's more important than actually all the syntax jargon is your overall mindset.

In Python, you need to think of yourself as a problem solver,

not just a coder.

You see, code is a tool.

It's a mechanism for you to solve bigger problems.

In fact, back in the day when software engineering was first getting started,

they used physical punch cards.

And coding languages like Python in Java came much later as a mechanism of abstraction to help us write faster,

more efficient solutions for our problem.

Imagine if you're an artist.

If you're a good artist,

you want to create visually aesthetic pieces.

And you should be skilled enough to do that with a paintbrush,

or a crayon, or a color pencil.

Everything is just a tool at your disposal.

And so if you want to become really good at coding in computer science,

it shouldn't matter what language you pick up.

The art of you problem solving will forever stay the same.

Now that we've laid down our foundations,

let's get to the setif layer,

and here's where a lot of beginners get lost.

A lot of people at this point will tell you,

oh, go to your terminal and install brew,

then download Python, then download Anaconda,

download PIP, download Spyder, download PyTarm,

download VS Code, and they'll tell you so many different things to download,

because it'll make you this complete high quality programmer.

Well guess what, all of that stuff is useless at this stage.

You know why?

Because I've been through it.

download all these different configurations as a beginner programmer,

you actually don't even know how to set things up properly.

When I was first learning,

I downloaded everything and I tried to run Python,

but apparently there's a difference between Python 2 and Python 3.

And then when you type Python into the terminal,

all of a sudden you have to write out code

and then you have to know the Python function to exit it was a complete mess

and I spent more time trying to be able to write Python code than actually writing Python code

but

if you want to learn Python quickly the right way the

first thing I would do is actually go to Google Colab

all you need is a Google account like a Gmail then you open up this environment

and you can run any code with any library you would ever need Plus,

you don't have to deal with download configuration issues since it handles all of that.

Like imagine being a chef and you walk into a kitchen that has every single ingredient you would ever need.

Plus, it has pots, pans,

dishes, and spatula, Michelin grade quality.

Everything you would ever want right there,

yeah, I know you'd feel good.

So that's your environment.

But in terms of the actual tasks you should do at this point,

I want you to check out this GitHub repository right here called 30 Days of Python.

It's a It's a repository that contains all the fundamental basic Python lessons,

and it'll get you all the way to an intermediate level.

It starts with your basics and data structures,

and then it goes to Python and web applications and API calls.

Plus, with every lesson, it gives you tutorials,

sample codes, so you can actually take all the information you're getting and execute it in your Google Colab.

But beyond just that, I would also recommend using Kodi.tech.

This platform gives you a lot of mini bite-sized lessons with actual projects for each one.

It's great for Python because it shows you,

once again, the lessons and allows you to practice coding in their sandbox environment.

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Plus it has a mini AI tutor that can help you out if you're ever struggling with anything.

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And the best part is they're completely free,

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but they have some premium features like unlimited AI queries.

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And if you're interested in that,

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I actually have a discount link in the description down below.

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Anyways, once you've got a solid grasp of Python,

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then you can actually go ahead and download all those like complicated configurations into your home laptop environment.

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You can download Python 3 and install a Visual Studio Code or PyCharm.

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Really quick, I want you to check out this new Dell Pro Max Tower T2 I recently got.

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It's an AI powerhouse.

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The Dell Pro Max Tower T2,

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It features the industry's only new chassis designed specifically for AI workloads.

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These specs enable faster training,

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smoother inferencing, and real-time performance for the most demanding workflows.

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With 96 gigabytes of lightning fast memory,

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it delivers the bandwidth and compute power needed for advanced data visualization,

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3D rendering, and generative AI development.

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Simply put, it's the ultimate GPU for creators,

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developers, and engineers pushing the limits of what AI hardware can do.

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This is what future-proofing your workflow looks like,

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taking the advantages of AI in a high-performing environment.

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Step into the future of work with the Dell Pro Max Tower T2.

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Explore more at Dell.com today.

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And now back to the video.

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Now let's get ready to move even faster,

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and the next layer of our pyramid is real-world projects.

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You can't claim to be a good chef and not cook anything,

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and you can't claim to be a good coder and not build anything.

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We want to see some results quickly.

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So one of the best resources I found,

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especially nowadays for learning Python,

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is this GitHub repository called Practical Tutorials Project Based Learning.

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A lot of people get stuck in this thing called tutorial hell,

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where they watch every video,

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read every textbook, absorb every tutorial over and over and over again until they burn themselves out.

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The way you avoid this is by actually starting to build something.

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This way you can see the fruits of your labor

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and you know for a fact that you're making progress and not burning yourself out.

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So go to this repository,

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click Python, and you'll see projects like web scraping,

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web applications, bots, and even data science type projects.

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And within them, it goes step by step on how to build these projects.

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So even if you have no experience whatsoever,

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you will build experience through that.

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And the goal for you throughout each project should be thinking,

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how can I take this tutorial and amp it up?

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For example, maybe I don't want to create a Reddit bot,

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but maybe I want to create a similar bot

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that can actually scan the market and find the best time to buy a stock.

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All of a sudden, you took this baseline project and transformed it to something that's actually impressive.

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Then you can put it on your resume and actually use it to get hired.

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But if you want to take it even a step above that and really showcase your depth of expertise in Python,

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there's this GitHub repository right here called Build Your Own X.

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Once again, it has so many advanced level project ideas for actually many different coding languages.

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But if we just look at the Python one,

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it shows you, for example,

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how to create a Python interpreter.

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And it gives you the framework to build bots,

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to build databases, to build containers.

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A lot of the work

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that you do throughout these projects will set up the infrastructure for you to create startups or really cool apps one day.

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And guess what?

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Maybe you start working on this project because hopefully might get you a job.

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But better yet, if you're able to create a startup through this,

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you might be so successful that you don't even need a job.

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And that's the type of mental framework that I want you to have.

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Because with so many resources at your grasp,

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you can literally do anything.

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So by this point, you should be intermediate with Python.

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You have your baseline and solid projects now.

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But if you really want to accelerate your workflow,

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we need to talk about the next layer,

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which is actually the hippopotamus in the room, and that is AI.

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A lot of people say that it might not be worth learning to code because of AI.

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It might not be worth studying computer science.

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This whole video might be useless because AI is going to take over.

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Well, actually, Python is the best language to learn in this AI world.

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Because a lot of machine learning,

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AI development work and applications are run through Python.

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Python has so many popular libraries like scikit-learn,

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pandas, pytorch that actually help build machine learning models.

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And actually being that expert in Python will actually give you a unique advantage in this AI world.

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So how do you build that advantage?

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Well two things.

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Use AI as a partner and use AI as the product.

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So on the partner front,

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this means using a variety of different coding assistants to get your job done.

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For example, Cursor is an IDE that I started using instead of VS Code

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because it can scan through your whole code base and make code adjustments very quickly.

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Then it'll actually generate files and files of Python code very quickly.

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But if you're not there yet,

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then GitHub Copilot is also great.

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And I found this really useful for the scenario where you know how to solve the problem,

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but you don't remember exactly,

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oh, what was that function called again?

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What was that type called again?

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So this is kind of like your mini assistant to help you code faster.

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Now, I want to be very, very clear.

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I'm only recommending using AI once you have become an intermediate level in Python.

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Because when you're still trying to learn the basics, it's not really good.

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It's kind of like cheating on your homework.

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You learn nothing.

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But once you have the experience,

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it will actually accelerate you so much further.

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Just like no one learns to drive using a Lamborghini,

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but once you have enough driving experience,

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you can drive that car to accelerate your driving experience.

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The second part of taking advantage of AI is using AI on the product standpoint.

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You need to dive deep into the world of AI,

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machine learning, and deep learning,

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and start dabbling with Python libraries such as NumPy,

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scikit-learn, and do some really cool projects.

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One of my first ever real machine learning projects was creating a COVID-19 death predictor.

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It was during the pandemic I gathered a bunch of data across a lot of different countries about their population sizes,

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densities, and the impact of COVID-19.

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Then I processed it using NumPy and plotted graphs using Matplotlib.

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And so visually, we got to see a heat map of the impacts of the pandemic all across the world.

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Plus, it was an excellent talking point on resumes and interviews.

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And I actually used that to help me land the internships and jobs that I got afterwards.

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But that was many, many years ago.

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Nowadays, there's so much you can do with the power of AI, generative AI.

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For example, you can create a chatbot that purchases items on Amazon.com.

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So you don't have to go through the nitty gritties of finding a product,

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adding your payment information and processing it.

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Or you can create a machine learning recommendation system so that anytime you're in a new city,

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you automatically get a text message of the best restaurants in your area that are tailored to your taste buds.

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But the bigger picture I really want you to think about is once you create those products,

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package it, put it in an app,

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throw it on the app store.

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And I have seen people literally make millions of dollars doing such things.

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Or a step below that,

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throw that project onto your resume,

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add it to your LinkedIn,

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and use it to actually help yourself get tired.

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Alright, so finally, we've made it to the top of the pyramid,

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and at this point, you have effectively learned Python pretty well.

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But the goal for you isn't to just learn to code,

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but rather, continuous mastery.

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Staying the master of Python coding.

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Just like a lot of things in life,

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your goal isn't to get married,

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it is to stay married.

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The goal isn't to get in shape,

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it is to stay in shape.

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It is to master that practice.

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So that means there is a continuous effort that you need to put week in,

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week out, day in, day out to stay at the top of your game,

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especially with coding in Python.

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So to do that, you should try the brain dump protocol.

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Every night, once you're done coding, turn off your laptop.

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Take out a piece of paper and pen and write down for five minutes.

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What did you do that day?

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What went wrong?

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What went right?

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And what is something that you learned?

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And it might seem a little bizarre at first,

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like why are we writing this down?

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But there's actually a deeper purpose.

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First, you actually bring yourself back to reality and put your thoughts together.

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Second, it gives you a sense of accomplishment because you work so hard throughout the day.

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Third, it tells you exactly what you need to tackle the next day.

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And fourth, over time, you'll be able to stack up your progress

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and see what used to bother you then actually became a highlight for today.

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And it'll reinforce internally the idea of your mastery.

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If you want to lose weight fast,

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you track all your calories and keep a food diary.

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Just like that, if you want to learn to code Python fast,

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you better keep a coding diary to track your progress.

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And beyond just this, anytime you do anything cool or remarkable,

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post about it online.

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One of my friends for his last two jobs never even had to apply.

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Because anytime he did a cool coding project,

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he would post about it on Twitter.

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And hiring managers would reach out to him because they see the amazing work that he does.

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So once again, with the power of Python,

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with the power of AI,

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and honestly, with the power of the internet,

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you have so many golden opportunities.

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Honestly, it's so exciting.

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Well, that's about all I have in this video.

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I really hope that you guys enjoyed it.

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And if you did, make sure to hit the like button,

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subscribe if you haven't already.

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And if you're interested in finding out what do software engineers do on a day-to-day basis,

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FOR YOU

맥락 및 배경

이 비디오는 2019년에 발표된 내용으로, 발표자는 두 달이라는 짧은 시간 안에 Python을 마스터해야 하는 상황에 처하게 됩니다. 그는 전혀 경험이 없는 상태에서 AI 프로젝트에 참여하기 위해 Python을 학습하게 되었으며, 이 과정에서 중요한 내용과 팁을 공유합니다. Python의 기본 개념과 언어에 대한 올바른 접근 방식을 습득하는 것이 중요하다는 점을 강조하고 있습니다. 이러한 내용은 영어 회화 연습에 있어서도 비슷한 맥락으로 적용될 수 있습니다.

일상 소통을 위한 TOP 5 표현

기본 문법: Python의 구문을 배우기 전에는 코드 작성을 시작하지 않는 것이 중요하다.
제로 인덱스: Python에서는 리스트의 첫 번째 항목이 0번째 인덱스에 위치한다.
조건문 사용: 주어진 조건이 충족될 때까지 계속해서 행동하는 것이 필요하다.
문제 해결자: 코드를 쓰는 것 이상의 역할로서 문제를 해결하는 사고를 중요시해야 한다.
예술적 접근: 자신을 하나의 예술가로 여기고, 다양한 도구를 통해 문제를 해결하는 능력을 길러야 한다.

단계별 쉐도잉 가이드

이 비디오를 효과적으로 활용하고자 하는 영어 학습자에게는 쉐도잉 영어 회화 연습이 매우 유용합니다. 아래에 단계별로 접근하는 방법을 제안합니다.

비디오 시청: 먼저, 비디오를 한번 전체적으로 시청하며 내용을 이해합니다.
부분 나누기: 내용을 몇 개의 짧은 섹션으로 나누어 각 섹션마다 반복 시청합니다.
반복 쉐도잉: 각 섹션을 들으면서 말소리에 맞춰서 따라서 말해보세요. 이때 발음과 억양에 신경을 씁니다.
해석 연습: 쉐도잉 후, 내용을 요약하거나 자신의 생각으로 다시 설명해보세요. 이를 통해 이해도를 높일 수 있습니다.
반복 학습: 매일 짧은 시간을 정해 놓고 위의 과정을 반복하며 꾸준히 연습합니다.

이와 같은 방법으로 영어 쉐도잉을 하면, 단순히 코드와 조건문 학습에 그치지 않고, 실질적인 의사소통 능력을 향상시키는 데 큰 도움이 됩니다. 따라서, IELTS 스피킹과 같은 시험 대비에도 유리합니다.

이 영상의 문법

화자가 가장 많이 쓰는 문형을 영상 속 실제 표현과 함께 정리했습니다.

문형	영상 속 표현
현재완료 have/has + 과거분사 — 과거의 일이 지금도 관련이 있을 때	we've laid · I've been · have become
수동태 be + 과거분사 — 누가 하는지보다 무슨 일이 일어나는지에 초점	being said · should be skilled · are run

쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법

쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.

섀도잉 방법: 단계별 전체 가이드 읽기 →

쉐도잉 연습: How I Would Learn Python FAST (if I could start over) - 영상으로 영어 말하기 배우기

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맥락 및 배경

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단계별 쉐도잉 가이드

이 영상의 문법

쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법