Pratica di 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!)】 - Impara a parlare inglese con i video
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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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Vocabolario e note di pronuncia per questa lezione
Questa lezione di conversazione di livello C1 si basa sul video “P1 机器学习的应用【2024公认最好的”. Le parole che ritornano più spesso: machine, learning, algorithms, applications, learn. Questo video contiene 43 frasi e 698 parole da ripetere con lo shadowing. Il parlato dura 4:26. Chi parla ha un ritmo naturale di circa 158 parole al minuto, vicino a una conversazione quotidiana. Il 85% delle parole rientra nelle 3.000 più comuni dell’inglese; conviene studiare le altre prima di iniziare.
Vocaboli chiave di questo video
Le 11 parole più avanzate del video, con pronuncia e significato:
| Parola | Pronuncia | Significato |
|---|---|---|
| algorithm sostantivo | /ˈælɡəɹɪðm̩/ | algoritmo |
| excite verbo | /ɪkˈsaɪt/ | stimolare, eccitare |
| augment verbo | /ɔɡˈmɛnt/ | aumentare, crescere |
| dabble verbo | /ˈdæb.əl/ | sguazzare |
| diagnose verbo | /ˈdaɪ.əɡˌnoʊs/ | diagnosticare |
| terminology sostantivo | /ˌtɝ.məˈnɑ.lə.d͡ʒi/ | terminologia |
| worthwhile aggettivo | /ˌwɜː(ɹ)θˈwaɪl/ | che vale la pena |
| researcher sostantivo | /ˈɹiˌsɝ.t͡ʃɚ/ | ricercatore, ricercatrice |
| automotive aggettivo | /ˌɔtəˈmoʊtɪv/ | automobilistico |
| explicit aggettivo | /ɪkˈsplɪs.ɪt/ | specifico, proprio |
| tremendous aggettivo | /tɹɪˈmɛndəs/ | formidabile, tremendo |
I phrasal verb che sentirai
| Parola | Significato |
|---|---|
| grow up verbo | crescere |
| pick up verbo | tirare su, sollevare |
| turn out verbo | risultare |
La grammatica di questo video
Le strutture che chi parla usa di più, con le parole esatte del video:
| Struttura | Nel video |
|---|---|
| Forma passiva be + participio passato — conta ciò che accade, non chi lo fa | is sometimes called · has been overhyped · is estimated |
| Present perfect have/has + participio passato — un’azione passata che conta ancora adesso | you've learned · has been overhyped |
Pronuncia a cui fare attenzione
Chi parla usa 7 contrazioni e forme ridotte, come we'll, don't, I've. Pronunciale nella forma breve, così come le senti.
- I suoni “th”: algorithm /ˈælɡəɹɪðm̩/, worthwhile /ˌwɜː(ɹ)θˈwaɪl/
- Parole lunghe — attenzione all’accento: terminology /ˌtɝ.məˈnɑ.lə.d͡ʒi/, automotive /ˌɔtəˈmoʊtɪv/
I suoni difficili per chi parla italiano:
- Consonante finale — senza aggiungere una vocale dopo: excite /ɪkˈsaɪt/, augment /ɔɡˈmɛnt/, diagnose /ˈdaɪ.əɡˌnoʊs/, automotive /ˌɔtəˈmoʊtɪv/, explicit /ɪkˈsplɪs.ɪt/
- /æ/ — più aperta della “e”: algorithm /ˈælɡəɹɪðm̩/, dabble /ˈdæb.əl/, vastly /ˈvæstli/
Come esercitarsi con questo video
- Ascolta tutto il video una volta senza parlare e annota le parole che non conosci.
- Inizia a velocità 0,75×, fai shadowing frase per frase e torna alla velocità normale quando diventa facile.
- Registrati e confronta con l’originale, facendo attenzione a parole come algorithm, excite, augment.
Cos'è la tecnica dello Shadowing?
Shadowing è una tecnica di apprendimento delle lingue supportata da studi scientifici, originariamente sviluppata per la formazione dei traduttori professionisti e resa popolare dal poliglotta Dr. Alexander Arguelles. Il metodo è semplice ma potente: ascolti un audio in inglese di madrelingua e lo ripeti immediatamente ad alta voce — come un'ombra che segue il parlante con un ritardo di solo 1–2 secondi. A differenza dell'ascolto passivo o degli esercizi di grammatica, lo shadowing costringe il tuo cervello e i muscoli della bocca a elaborare e riprodurre simultaneamente i modelli di discorso reale. La ricerca dimostra che migliora significativamente la precisione della pronuncia, l'intonazione, il ritmo, il discorso connesso, la comprensione dell'ascolto e la fluidità del parlato — rendendolo uno dei metodi più efficaci per la preparazione alla prova di speaking dell'IELTS e per la comunicazione reale in inglese.