Prática de Shadowing: 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!)】 - Aprenda a falar inglês com vídeo
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In this class, you learn about the state of the art and also practice implementing machine learning algorithms yourself.
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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,
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and you get a sense of what is the state of the art in AI.
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Beyond learning the algorithms, though, in this class, you also learn all the important practical tips and tricks for making them perform well,
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and you get to implement them and see how they work for yourself.
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So why is machine learning so widely used today?
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Machine learning had grown up as a subfield of AI or artificial intelligence.
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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,
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like in your GPS.
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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,
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recognize human speech, diagnose diseases from x-rays, or build a self-driving car.
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The only way we knew how to do these things was to have a machine learn to do it by itself.
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For me, when I founded and was leading the Google Brain team, I worked on problems like speech recognition, computer vision for Google Maps,
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street view images, and advertising.
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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.
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Most recently, Atlantic AI, AI funded at Stanford University, I've been getting to work on AI applications in manufacturing,
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large-scale agriculture, healthcare, e-commerce, and other problems.
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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.
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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.
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In fact, I find it hard to think of any industry
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that machine learning is unlikely to touch in a significant way now or in the near future.
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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.
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This is sometimes called Artificial General Intelligence, or AGI.
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I think AGI has been overhyped and we're still a long way away from that goal.
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I don't know if it'll take 50 years or 500 years or longer to get there,
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but most AI researchers believe that the best way to get closer to that goal is by using learning algorithms,
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maybe ones that take some inspiration from how the human brain works.
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You also hear a little more about this quest for AGI later in this course.
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According to a study by McKinsey,
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AI and machine learning is estimated to create an additional $13 trillion of value annually by the year 2030.
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Even though machine learning is already creating tremendous amounts of value in the software industry, I think there could be even vastly greater value
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that is yet to be created outside the software industry in sectors such as retail,
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travel, transportation, automotive, materials, manufacturing, and so on.
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Because of the massive untapped opportunities across so many different sectors,
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today there is a vast unfulfilled demand for this skill set.
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That's why this is such a great time to be learning about machine learning.
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If you find machine learning applications exciting, I hope you stick with me through this class.
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I can almost guarantee that you find mastering these skills worthwhile.
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In the next video, we'll look at a more formal definition of what is machine learning,
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and we'll begin to talk about the main types of machine learning problems and algorithms.
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You pick up some of the main machine learning terminology
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and start to get a sense of what are the different algorithms and when each one might be appropriate.
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So let's go on to the next video.
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Vocabulário e dicas de fala para esta lição
Esta aula de conversação de nível C1 usa o vídeo “P1 机器学习的应用【2024公认最好的”. As palavras que mais se repetem: machine, learning, algorithms, applications, learn. Este vídeo tem 43 frases e 698 palavras para praticar shadowing. A fala dura 4:26. O falante fala em ritmo natural, cerca de 158 palavras por minuto, próximo de uma conversa do dia a dia. 85% das palavras estão entre as 3.000 mais comuns do inglês; vale a pena estudar o restante antes de começar.
Vocabulário principal deste vídeo
As 13 palavras mais avançadas do vídeo, com pronúncia e significado:
| Palavra | Pronúncia | Significado |
|---|---|---|
| algorithm substantivo | /ˈælɡəɹɪðm̩/ | algoritmo |
| excite verbo | /ɪkˈsaɪt/ | animar, empolgar |
| augment verbo | /ɔɡˈmɛnt/ | aumentar |
| dabble verbo | /ˈdæb.əl/ | umedecer |
| diagnose verbo | /ˈdaɪ.əɡˌnoʊs/ | diagnosticar |
| subfield substantivo | subárea, subcampo | |
| terminology substantivo | /ˌtɝ.məˈnɑ.lə.d͡ʒi/ | terminologia |
| worthwhile adjetivo | /ˌwɜː(ɹ)θˈwaɪl/ | vantajoso |
| researcher substantivo | /ˈɹiˌsɝ.t͡ʃɚ/ | pesquisador, pesquisadora |
| automotive adjetivo | /ˌɔtəˈmoʊtɪv/ | automotivo |
| explicit adjetivo | /ɪkˈsplɪs.ɪt/ | explícito |
| someday advérbio | um dia | |
| tremendous adjetivo | /tɹɪˈmɛndəs/ | tremendo, formidável |
Phrasal verbs que você vai ouvir
| Palavra | Significado |
|---|---|
| grow up verbo | crescer |
| pick up verbo | pegar |
| turn out verbo | terminar |
Gramática neste vídeo
As estruturas que o falante mais usa, com as palavras exatas do vídeo:
| Estrutura | No vídeo |
|---|---|
| Voz passiva be + particípio passado — o foco está no que acontece, não em quem faz | is sometimes called · has been overhyped · is estimated |
| Present perfect have/has + particípio passado — uma ação passada que ainda importa agora | you've learned · has been overhyped |
Pronúncia para ficar de olho
O falante usa 7 contrações e formas reduzidas, como we'll, don't, I've. Diga-as na forma curta, do jeito que você ouve.
- Os sons de “th”: algorithm /ˈælɡəɹɪðm̩/, worthwhile /ˌwɜː(ɹ)θˈwaɪl/
- Palavras longas — acerte a sílaba tônica: terminology /ˌtɝ.məˈnɑ.lə.d͡ʒi/, automotive /ˌɔtəˈmoʊtɪv/
Sons difíceis para falantes de português:
- Consoante final — sem acrescentar um “i” depois: excite /ɪkˈsaɪt/, augment /ɔɡˈmɛnt/, explicit /ɪkˈsplɪs.ɪt/
- /l/ final — a língua toca o céu da boca, não vira “u”: dabble /ˈdæb.əl/, worthwhile /ˌwɜː(ɹ)θˈwaɪl/
Como praticar com este vídeo
- Ouça o vídeo inteiro uma vez sem falar e anote as palavras que você não conhece.
- Comece na velocidade 0,75×, faça shadowing frase por frase e volte à velocidade normal quando ficar fácil.
- Grave a sua voz e compare com o original, prestando atenção a palavras como algorithm, excite, augment.
O que é a Técnica de Shadowing?
Shadowing é uma técnica de aprendizado de idiomas com base científica, originalmente desenvolvida para o treinamento de intérpretes profissionais. O método é simples, mas poderoso: você ouve áudio em inglês nativo e repete imediatamente em voz alta — como uma sombra seguindo o falante com 1-2 segundos de atraso. Pesquisas mostram melhora significativa na precisão da pronúncia, entonação, ritmo, sons conectados, compreensão auditiva e fluência na fala.