쉐도잉 연습: P1 机器学习的应用【2024公认最好的 | 吴恩达机器学习 | 教程 | Machine Learning Specialization(超爽中英!)】 - 영상으로 영어 말하기 배우기

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In this class, you learn about the state of the art and also practice implementing machine learning algorithms yourself.

You learn about the most important machine learning algorithms, some of which are exactly what's being used in large AI or large tech companies today,

and you get a sense of what is the state of the art in AI.

Beyond learning the algorithms, though, in this class, you also learn all the important practical tips and tricks for making them perform well,

and you get to implement them and see how they work for yourself.

So why is machine learning so widely used today?

Machine learning had grown up as a subfield of AI or artificial intelligence.

We wanted to build intelligent machines, and it turns out that there are a few basic things that we could program a machine to do, such as how to find the shortest path from A to B,

like in your GPS.

But for the most part, we just did not know how to write an explicit program to do many of the more interesting things, such as perform web search,

recognize human speech, diagnose diseases from x-rays, or build a self-driving car.

The only way we knew how to do these things was to have a machine learn to do it by itself.

For me, when I founded and was leading the Google Brain team, I worked on problems like speech recognition, computer vision for Google Maps,

street view images, and advertising.

Or leading AI at Baidu, I worked on everything from AI for augmented reality to combating payment fraud to leading a self-driving car team.

Most recently, Atlantic AI, AI funded at Stanford University, I've been getting to work on AI applications in manufacturing,

large-scale agriculture, healthcare, e-commerce, and other problems.

Today, there are hundreds of thousands, perhaps millions of people, working on machine learning applications who could tell you similar stories about their work with machine learning.

When you've learned these skills, I hope that you too will find it great fun to dabble in exciting different applications and maybe even different industries.

In fact, I find it hard to think of any industry

that machine learning is unlikely to touch in a significant way now or in the near future.

Looking even further into the future, many people, including me, are excited about the AI dream of someday building machines as intelligent as you or me.

This is sometimes called Artificial General Intelligence, or AGI.

I think AGI has been overhyped and we're still a long way away from that goal.

I don't know if it'll take 50 years or 500 years or longer to get there,

but most AI researchers believe that the best way to get closer to that goal is by using learning algorithms,

maybe ones that take some inspiration from how the human brain works.

You also hear a little more about this quest for AGI later in this course.

According to a study by McKinsey,

AI and machine learning is estimated to create an additional $13 trillion of value annually by the year 2030.

Even though machine learning is already creating tremendous amounts of value in the software industry, I think there could be even vastly greater value

that is yet to be created outside the software industry in sectors such as retail,

travel, transportation, automotive, materials, manufacturing, and so on.

Because of the massive untapped opportunities across so many different sectors,

today there is a vast unfulfilled demand for this skill set.

That's why this is such a great time to be learning about machine learning.

If you find machine learning applications exciting, I hope you stick with me through this class.

I can almost guarantee that you find mastering these skills worthwhile.

In the next video, we'll look at a more formal definition of what is machine learning,

and we'll begin to talk about the main types of machine learning problems and algorithms.

You pick up some of the main machine learning terminology

and start to get a sense of what are the different algorithms and when each one might be appropriate.

So let's go on to the next video.

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P2 什么是机器学习【2024公认最好的 | 吴恩达机器学习 | 教程 | Machine Learning Specialization(超爽中英!)】

Daily ConversationUniversity Students

FOR YOU

Vocabulary and speaking notes for this lesson

This C1 speaking lesson is built on the video “P1 机器学习的应用【2024公认最好的”. The speaker keeps coming back to these words: machine, learning, algorithms, applications, learn. This video has 43 sentences and 698 words to shadow. The speech runs for 4:26. The speaker talks at a natural 158 words per minute, close to everyday conversation. 85% of the words are among the 3,000 most common in English; the rest is worth studying before you start.

Key vocabulary in this video

The 15 most advanced words in the video, with pronunciation and meaning:

Word	Pronunciation	Meaning
algorithm noun	/ˈælɡəɹɪðm̩/	A collection of ordered steps that solve a mathematical problem. A precise step-by-step plan for a computational procedure that possibly begins with an input…
excite verb	/ɪkˈsaɪt/	To stir the emotions of; to cause to feel excitement.
augment verb	/ɔɡˈmɛnt/	To increase; to make larger or supplement.
dabble verb	/ˈdæb.əl/	To make slightly wet or soiled by spattering or sprinkling a liquid (such as water, mud, or paint) on it; to bedabble.
diagnose verb	/ˈdaɪ.əɡˌnoʊs/	To determine which disease is causing a sick person's signs and symptoms; to find the diagnosis.
overhype verb		To promote or publicize excessively.
subfield noun		A smaller, more specialized area of study or occupation within a larger one
unfulfilled adjective		Lacking fulfillment; marked by a feeling of failure to achieve goals or desires.
untapped adjective		Not tapped; not drawn on in terms of resources.
vastly adverb	/ˈvæstli/	Greatly, in a vast manner.
terminology noun	/ˌtɝ.məˈnɑ.lə.d͡ʒi/	A treatise on terms, especially those used in a specialised field.
worthwhile adjective	/ˌwɜː(ɹ)θˈwaɪl/	Good and important enough to spend time, effort, or money on.
researcher noun	/ˈɹiˌsɝ.t͡ʃɚ/	One who researches.
automotive adjective	/ˌɔtəˈmoʊtɪv/	Having the ability to move by itself; self-propelled or self-propelling.
explicit adjective	/ɪkˈsplɪs.ɪt/	Very specific, clear, or detailed.

Phrasal verbs you will hear

Word	Meaning
grow up verb	To mature and become an adult.
pick up verb	To lift; to grasp and raise.
turn out verb	To end up; to result.

이 영상의 문법

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

문형	영상 속 표현
수동태 be + 과거분사 — 누가 하는지보다 무슨 일이 일어나는지에 초점	is sometimes called · has been overhyped · is estimated
현재완료 have/has + 과거분사 — 과거의 일이 지금도 관련이 있을 때	you've learned · has been overhyped

Pronunciation to watch

The speaker uses 7 contractions and reduced forms, such as we'll, don't, I've. Say them the short way, as you hear them.

The “th” sounds: algorithm /ˈælɡəɹɪðm̩/, worthwhile /ˌwɜː(ɹ)θˈwaɪl/
Long words — get the stress right: terminology /ˌtɝ.məˈnɑ.lə.d͡ʒi/, automotive /ˌɔtəˈmoʊtɪv/

How to practise with this video

Listen to the whole video once without speaking and note the words you do not know.
Start at 0.75× speed, shadow it sentence by sentence, then go back to normal speed once it feels easy.
Record yourself and compare with the original, paying attention to words like algorithm, excite, augment.

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.

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