シャドーイング練習: 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!)】 - 動画で英語スピーキングを学ぶ
練習モードを選ぶ
各モードは全画面の練習ルームで開きます。
43 文
文が短すぎたり長すぎる場合は、Editをタップして調整してください。
1
In this class, you learn about the state of the art and also practice implementing machine learning algorithms yourself.
2
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,
3
and you get a sense of what is the state of the art in AI.
4
Beyond learning the algorithms, though, in this class, you also learn all the important practical tips and tricks for making them perform well,
5
and you get to implement them and see how they work for yourself.
6
So why is machine learning so widely used today?
7
Machine learning had grown up as a subfield of AI or artificial intelligence.
8
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,
9
like in your GPS.
10
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,
11
recognize human speech, diagnose diseases from x-rays, or build a self-driving car.
12
The only way we knew how to do these things was to have a machine learn to do it by itself.
13
For me, when I founded and was leading the Google Brain team, I worked on problems like speech recognition, computer vision for Google Maps,
14
street view images, and advertising.
15
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.
16
Most recently, Atlantic AI, AI funded at Stanford University, I've been getting to work on AI applications in manufacturing,
17
large-scale agriculture, healthcare, e-commerce, and other problems.
18
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.
19
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.
20
In fact, I find it hard to think of any industry
21
that machine learning is unlikely to touch in a significant way now or in the near future.
22
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.
23
This is sometimes called Artificial General Intelligence, or AGI.
24
I think AGI has been overhyped and we're still a long way away from that goal.
25
I don't know if it'll take 50 years or 500 years or longer to get there,
26
but most AI researchers believe that the best way to get closer to that goal is by using learning algorithms,
27
maybe ones that take some inspiration from how the human brain works.
28
You also hear a little more about this quest for AGI later in this course.
29
According to a study by McKinsey,
30
AI and machine learning is estimated to create an additional $13 trillion of value annually by the year 2030.
31
Even though machine learning is already creating tremendous amounts of value in the software industry, I think there could be even vastly greater value
32
that is yet to be created outside the software industry in sectors such as retail,
33
travel, transportation, automotive, materials, manufacturing, and so on.
34
Because of the massive untapped opportunities across so many different sectors,
35
today there is a vast unfulfilled demand for this skill set.
36
That's why this is such a great time to be learning about machine learning.
37
If you find machine learning applications exciting, I hope you stick with me through this class.
38
I can almost guarantee that you find mastering these skills worthwhile.
39
In the next video, we'll look at a more formal definition of what is machine learning,
40
and we'll begin to talk about the main types of machine learning problems and algorithms.
41
You pick up some of the main machine learning terminology
42
and start to get a sense of what are the different algorithms and when each one might be appropriate.
43
So let's go on to the next video.
アプリをダウンロード
流暢に話すために必要なすべて
AI発音採点文ごとに採点
IPA練習すべての音をマスター
単語学習自分の単語帳を作る
単語ゲーム遊びながら覚える
同じチャンネル
FOR YOU
おすすめ動画
C11
105 文
Google’s AI Course for Beginners (in 10 minutes)!
American AccentBusiness EnglishUniversity StudentsWorking Adults
C11
315 文
how AI broke the SWE Career Ladder
Business EnglishInterview EnglishUniversity Students
C1465
25 文
The Human Economy in the Age of AI | Ben Pascut | TEDx
IELTS PrepBusiness EnglishUniversity Students
C12
144 文
WWDC26: Run local agentic AI on the Mac using MLX | Apple
Business EnglishUniversity StudentsWorking Adults
C148
8 文
Neurosymbolic AI Explained
Business EnglishUniversity StudentsWorking Adults
C13
208 文
AI & Education: Generative AI & the Future of Critical Thinking
Business EnglishUniversity StudentsWorking Adults
C11
12 文
Rapid Engineering With AI
Business EnglishUniversity StudentsWorking Adults
C13
101 文
3.3k
Business EnglishInterview EnglishUniversity Students
C11
141 文
How AI is Changing Data Scientist's Workflow
Business EnglishUniversity StudentsWorking Adults
C13
54 文
Did Google just kickstart the intelligence explosion?
American AccentBusiness EnglishDaily ConversationUniversity Students
B22
163 文
Cách Mình Học Mọi Thứ Nhanh Gấp 10 Lần
Business EnglishUniversity StudentsWorking Adults
このレッスンの語彙とスピーキングのポイント
このC1レベルのスピーキングレッスンは、動画「P1 机器学习的应用【2024公认最好的」を教材にしています。 繰り返し出てくる語は次のとおりです：machine, learning, algorithms, applications, learn。 この動画には、シャドーイング用の文が43文、単語が698語あります。 音声の長さは4:26です。 話す速さは1分あたり約158語で、日常会話に近い自然なペースです。 単語の85%は英語の頻出3,000語に含まれます。残りは練習の前に確認しておきましょう。
この動画の重要語彙
動画の中で特に難しい単語10語を、発音と意味つきで紹介します。
| 単語 | 発音 | 意味 |
|---|---|---|
| algorithm 名詞 | /ˈælɡəɹɪðm̩/ | アルゴリズム, 演算手順 |
| excite 動詞 | /ɪkˈsaɪt/ | 興奮させる |
| augment 動詞 | /ɔɡˈmɛnt/ | 増やす |
| dabble 動詞 | /ˈdæb.əl/ | ばちゃばちゃさせる |
| diagnose 動詞 | /ˈdaɪ.əɡˌnoʊs/ | 診断する |
| terminology 名詞 | /ˌtɝ.məˈnɑ.lə.d͡ʒi/ | 用語, 専門語 |
| researcher 名詞 | /ˈɹiˌsɝ.t͡ʃɚ/ | 研究員, 研究者 |
| explicit 形容詞 | /ɪkˈsplɪs.ɪt/ | 明確, 明白 |
| someday 副詞 | いつか | |
| tremendous 形容詞 | /tɹɪˈmɛndəs/ | 莫大 |
動画に出てくる句動詞
| 単語 | 意味 |
|---|---|
| grow up 動詞 | 成長する, 大人に成る |
| pick up 動詞 | 拾う, 拾い上げる |
この動画の文法
話し手がよく使っている文型を、動画の実際の表現とともに紹介します。
| 文型 | 動画での表現 |
|---|---|
| 受動態 be + 過去分詞 — 誰がするかより、何が起きるかに焦点を当てる | is sometimes called · has been overhyped · is estimated |
| 現在完了形 have/has + 過去分詞 — 過去の出来事が今も関係している | you've learned · has been overhyped |
注意したい発音
話し手はwe'll, don't, I'veなど、短縮形や弱形を7回使っています。聞こえたとおりの短い形で発音しましょう。
- 「th」の音: algorithm /ˈælɡəɹɪðm̩/, worthwhile /ˌwɜː(ɹ)θˈwaɪl/
- 長い単語（アクセントの位置に注意）: terminology /ˌtɝ.məˈnɑ.lə.d͡ʒi/, automotive /ˌɔtəˈmoʊtɪv/
日本語話者が苦手な音：
- /r/ と /l/ の区別 — /l/ は舌先を歯茎につけ、/r/ はどこにもつけない: algorithm /ˈælɡəɹɪðm̩/, worthwhile /ˌwɜː(ɹ)θˈwaɪl/
- /v/ — /b/ にならないように、上の歯を下唇に当てる: vastly /ˈvæstli/, automotive /ˌɔtəˈmoʊtɪv/
- 子音の連続 — 間に母音を入れない: augment /ɔɡˈmɛnt/, vastly /ˈvæstli/, worthwhile /ˌwɜː(ɹ)θˈwaɪl/, explicit /ɪkˈsplɪs.ɪt/, tremendous /tɹɪˈmɛndəs/
この動画での練習方法
- まず声を出さずに動画を最後まで聞き、知らない単語をメモします。
- まず0.75倍速で一文ずつシャドーイングし、慣れてきたら通常の速度に戻します。
- 自分の声を録音して元の音声と比べます。algorithm, excite, augmentなどの単語に特に注意しましょう。
シャドーイングとは？英語上達に効果的な理由
シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。