跟讀練習: 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!)】 - 通過影片學習英語口語

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So what is machine learning?

In this video you learn a definition of what it is

and also get a sense of when you might want to apply it.

Let's take a look together.

Here's the definition of what is machine learning that is attributed to Arthur Samuel.

He defined machine learning as the field of study that gives computers the ability to learn without being explicitly programmed.

Salva's claim to fame was that back in the 1950s he wrote a checkers playing program.

And the amazing thing about this program was that Arthur Samuel himself wasn't a very good checkers player.

What he did was he had programmed a computer to play maybe tens of thousands of games against himself.

And by watching what sorts of board positions tended to lead to wins and what positions tended to lead to losses,

the checkers playing program learned over time what are good or bad board positions.

By trying to get to good and avoid bad positions, his program learned to get better and better at playing checkers.

Because the computer had the patience to play tens of thousands of games against itself,

it was able to get so much checkers playing experience that eventually it became a better checkers player than author Samuel himself.

Now, throughout these videos, besides me trying to talk about stuff,

I'll occasionally ask you a question to help make sure you understand the content here's one about what happens

if the computer had played far fewer games please take a look

and pick whichever you think is a better answer thanks for looking at the quiz and so

if you had selected this answer would have made it worse

then you got it right in general the more opportunities you give a learning algorithm to learn, the better it will perform.

If you didn't select the correct answer the first time, that's totally okay too.

The point of these quiz questions isn't to see if you can get them all correct on the first try.

These questions are here just to help you practice the concepts you're learning.

Author Samuel's definition was a rather informal one, but in the next two videos, we'll dive deeper together into what are the major types types of machine learning algorithms.

In this class, you learn about many different learning algorithms.

The two main types of machine learning are supervised learning and unsupervised learning.

We'll define what these terms mean more in the next couple videos.

Of these two, supervised learning is the type of machine learning that is used most in many real -world applications,

and that has seen the most rapid advancement and innovation.

In this specialization, which has three causes in total, the first and second causes will focus on supervised learning,

and the third will focus on unsupervised learning.

You might have also heard of reinforcement learning.

This is another type of machine learning algorithm that I'll talk about briefly,

but by far the two most used types of learning algorithms today are supervised learning and unsupervised learning.

That's why we'll spend most of this class talking about them.

The other thing we're going to spend a lot of time on in this specialization is practical advice for applying learning algorithms.

This is something I feel pretty strongly about.

Teaching about learning algorithms is like giving someone a set of tools

and equally important

or even more importance than making sure you have great tools is making sure you know how to apply them

because you know what good is it

if someone were to give you a state -of -the -art hammer

or a state -of -the -art hand drill

and say good luck now you have all the tools you

need to build a three -story house It doesn't really work like that.

And so too in machine learning, making sure you have the tools is really important.

And so it's making sure that you know how to apply the tools of machine learning effectively.

So that's what you get in this class, the tools as well as the skills in applying them effectively.

I regularly visit with friends and teams in some of the top tech companies.

And even today, I see experienced machine learning teams apply machine learning algorithms to some problems.

And sometimes they've been going at it for six months without much success.

And when I look at what they're doing, I sometimes feel like I could have told them six months ago that the current approach won't work.

And there's a different way of using these tools that will give them a much better chance of success.

So in this class, one of the relatively unique things you learn is you learn

a lot about the best practices for how to actually develop a practical, valuable machine learning system.

This way, you're less likely to end up in one of those teams

that end up losing six months going in the wrong direction.

In this class, you gain a sense of how the most skilled machine learning engineers build systems,

and I hope you finish this class as one of those very rare people in today's world

that know how to design and build serious machine learning systems.

So that's machine learning.

In the next video, let's look more deeply at what is supervised learning and also what is unsupervised learning.

In addition, you learn when you might want to use each of them, supervised and unsupervised learning.

I'll see you in the next video.

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本課的詞彙與口語要點

這段影片共有 64 個句子、927 個單詞可供跟讀。講話部分時長為 5:36。說話人語速自然，每分鐘約 166 個詞，接近日常對話。 88% 的單詞屬於英語最常用的 3,000 詞，其餘的詞建議在練習前先學一下。

影片中的重點詞彙

影片中 15 個值得學習的單詞，附發音和釋義：

單詞	發音	釋義
algorithm 名詞	/ˈælɡəɹɪðm̩/	演算法, 演算法
practical 形容詞	/ˈpɹæk.tɪ.kəl/	使用的, 實用 /實用
select 動詞	/sɪˈlɛkt/	選 /選, 挑選 /挑選
skill 名詞	/skɪl/	技巧, 技能
define 動詞	/dɪˈfaɪn/	界定
quiz 名詞	/kwɪz/	測驗 /測驗, 競猜 /競猜
deeply 副詞	/ˈdi(ː)pli/	深, 深深
equally 副詞	/ˈiːkwəli/	一樣地 /一樣地, 同樣地 /同樣地
occasionally 副詞	/əˈkeɪʒənəli/	偶爾 /偶爾, 偶然
rapid 形容詞	/ˈɹæp.ɪd/	快, 迅速
fame 名詞	/feɪm/	有名
innovation 名詞	/ˌɪn.əˈveɪ.ʃən/	革新, 創新 /創新
fewer	/ˈfjuː.ɚ/	較少 /較少, 更少
patience 名詞	/ˈpeɪ̯.ʃəns/	耐性, 忍耐
hammer 名詞	/ˈhæmɚ/	摃錘仔 /𫼱錘仔, 錘子 /錘子

影片中出現的短語動詞

單詞	釋義
end up 動詞	到達

需要注意的發音

說話人用了 16 次縮略和弱讀形式，例如 I'll, we'll, you're。請按聽到的簡短形式來說。

“sh” 和 “zh” 音: occasionally /əˈkeɪʒənəli/, innovation /ˌɪn.əˈveɪ.ʃən/, patience /ˈpeɪ̯.ʃəns/, specialization [ˌspɛʃəlaɪ̯ˈzeɪ̯ʃn̩]
長單詞——注意重音位置: occasionally /əˈkeɪʒənəli/, innovation /ˌɪn.əˈveɪ.ʃən/, specialization [ˌspɛʃəlaɪ̯ˈzeɪ̯ʃn̩], reinforcement /ˌɹiːɪnˈfɔːsmənt/

如何用這段影片練習

先完整聽一遍影片，不要開口，記下不認識的單詞。
先用 0.75 倍速逐句跟讀，熟練之後再回到正常速度。
錄下自己的聲音並與原聲對比，特別注意 algorithm, practical, select 這類單詞。

什麼是跟讀法？

跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧，最初開發用於專業口譯員的培訓，並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大：您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同，跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性，語調，節奏，連讀，聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。

影子跟讀法: 閱讀完整分步指南 →

跟讀練習: 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!)】 - 通過影片學習英語口語

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