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

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

⏸ 일시 정지

재생 속도:

285 문장

문장이 너무 짧거나 길면 Edit를 눌러 조정하세요.

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.

100

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

101

And the best part is they're completely free,

102

but they have some premium features like unlimited AI queries.

103

And if you're interested in that,

104

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

105

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

106

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

107

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

108

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

109

It's an AI powerhouse.

110

The Dell Pro Max Tower T2,

111

112

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

113

These specs enable faster training,

114

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

115

With 96 gigabytes of lightning fast memory,

116

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

117

3D rendering, and generative AI development.

118

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

119

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

120

This is what future-proofing your workflow looks like,

121

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

122

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

123

Explore more at Dell.com today.

124

And now back to the video.

125

Now let's get ready to move even faster,

126

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

127

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

128

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

129

We want to see some results quickly.

130

So one of the best resources I found,

131

especially nowadays for learning Python,

132

is this GitHub repository called Practical Tutorials Project Based Learning.

133

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

134

where they watch every video,

135

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

136

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

137

This way you can see the fruits of your labor

138

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

139

So go to this repository,

140

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

141

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

142

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

143

So even if you have no experience whatsoever,

144

you will build experience through that.

145

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

146

how can I take this tutorial and amp it up?

147

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

148

but maybe I want to create a similar bot

149

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

150

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

151

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

152

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

153

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

154

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

155

But if we just look at the Python one,

156

it shows you, for example,

157

how to create a Python interpreter.

158

And it gives you the framework to build bots,

159

to build databases, to build containers.

160

A lot of the work

161

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

162

And guess what?

163

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

164

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

165

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

166

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

167

Because with so many resources at your grasp,

168

you can literally do anything.

169

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

170

You have your baseline and solid projects now.

171

But if you really want to accelerate your workflow,

172

we need to talk about the next layer,

173

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

174

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

175

It might not be worth studying computer science.

176

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

177

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

178

Because a lot of machine learning,

179

AI development work and applications are run through Python.

180

Python has so many popular libraries like scikit-learn,

181

pandas, pytorch that actually help build machine learning models.

182

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

183

So how do you build that advantage?

184

Well two things.

185

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

186

So on the partner front,

187

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

188

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

189

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

190

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

191

But if you're not there yet,

192

then GitHub Copilot is also great.

193

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

194

but you don't remember exactly,

195

oh, what was that function called again?

196

What was that type called again?

197

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

198

Now, I want to be very, very clear.

199

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

200

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

201

It's kind of like cheating on your homework.

202

You learn nothing.

203

But once you have the experience,

204

it will actually accelerate you so much further.

205

Just like no one learns to drive using a Lamborghini,

206

but once you have enough driving experience,

207

you can drive that car to accelerate your driving experience.

208

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

209

You need to dive deep into the world of AI,

210

machine learning, and deep learning,

211

and start dabbling with Python libraries such as NumPy,

212

scikit-learn, and do some really cool projects.

213

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

214

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

215

densities, and the impact of COVID-19.

216

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

217

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

218

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

219

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

220

But that was many, many years ago.

221

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

222

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

223

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

224

adding your payment information and processing it.

225

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

226

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

227

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

228

package it, put it in an app,

229

throw it on the app store.

230

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

231

Or a step below that,

232

throw that project onto your resume,

233

add it to your LinkedIn,

234

and use it to actually help yourself get tired.

235

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

236

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

237

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

238

but rather, continuous mastery.

239

Staying the master of Python coding.

240

Just like a lot of things in life,

241

your goal isn't to get married,

242

it is to stay married.

243

The goal isn't to get in shape,

244

it is to stay in shape.

245

It is to master that practice.

246

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

247

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

248

especially with coding in Python.

249

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

250

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

251

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

252

What did you do that day?

253

What went wrong?

254

What went right?

255

And what is something that you learned?

256

And it might seem a little bizarre at first,

257

like why are we writing this down?

258

But there's actually a deeper purpose.

259

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

260

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

261

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

262

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

263

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

264

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

265

If you want to lose weight fast,

266

you track all your calories and keep a food diary.

267

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

268

you better keep a coding diary to track your progress.

269

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

270

post about it online.

271

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

272

Because anytime he did a cool coding project,

273

he would post about it on Twitter.

274

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

275

So once again, with the power of Python,

276

with the power of AI,

277

and honestly, with the power of the internet,

278

you have so many golden opportunities.

279

Honestly, it's so exciting.

280

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

281

I really hope that you guys enjoyed it.

282

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

283

subscribe if you haven't already.

284

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

285

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.

☕ 커피 한 잔 사주기

ShadowingEnglish는 여러분의 지원 덕분에 100% 무료로 운영됩니다. 서버 및 AI 유지 비용이 많이 듭니다. 여러분의 커피 한 잔이 큰 힘이 됩니다! ��

PayPal로 기부하기

ShadowingEnglish.com – 영어 쉐도잉 연습

쉐도잉(Shadowing)으로 원어민처럼 영어를 말하세요. YouTube 영상을 한 문장씩 듣고 따라 말하며, 진짜 발음과 유창성을 키우세요. IELTS 학습자들이 선택한 방법.

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

인기 동영상

맥락 및 배경

일상 소통을 위한 TOP 5 표현

단계별 쉐도잉 가이드

쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법

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

앱 다운로드

맥락 및 배경

일상 소통을 위한 TOP 5 표현

단계별 쉐도잉 가이드

쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법