Shadowing Practice: 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!)】 - Learn English Speaking with Video
Les maken...
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.
📺 Zelfde Kanaal
✨ Aanbevolen Video
C11
747 zinnen
MIT Introduction to Deep Learning | 6.S191
Business EnglishUniversity StudentsWorking Adults
C11
254 zinnen
AI Will Create New Wealth, But Not Where You Think | Carnegie Mellon University, Po-Shen Loh
Business EnglishDaily ConversationUniversity Students
C11
74 zinnen
LangChain in 5 Minutes (Explained Clearly)
Business EnglishUniversity StudentsWorking Adults
C11
177 zinnen
AWS Machine Learning Engineer Associate: What You Need to Know
Business EnglishUniversity StudentsWorking Adults
C16
10 zinnen
What's new in Google Cloud in less than 1 minute
Business EnglishUniversity StudentsWorking Adults
C18
114 zinnen
AI, Machine Learning, Deep Learning and Generative AI Explained
Business EnglishUniversity StudentsWorking Adults
C15
182 zinnen
How to Become an AI Product Manager in 2026 | Ex-Google, Microsoft
Business EnglishUniversity StudentsWorking Adults
C12
182 zinnen
We Let $200 GPT-Pro Think for 7 Days on Unsolved Math
Business EnglishUniversity StudentsWorking Adults
C11
315 zinnen
how AI broke the SWE Career Ladder
Business EnglishUniversity StudentsWorking Adults
C12
154 zinnen
歐巴馬談 AI：喊放慢的公司「是真的在擔心自己的產品」【AI 大人說】
Business EnglishDaily ConversationUniversity Students
C111
93 zinnen
Naive Bayes
Business EnglishUniversity StudentsWorking Adults
Woordenschat en spreektips bij deze les
Deze spreekles op niveau C1 is gebaseerd op de video “P1 机器学习的应用【2024公认最好的”. Deze woorden komen het vaakst terug: machine, learning, algorithms, applications, learn. Deze video bevat 43 zinnen en 698 woorden om na te spreken. Het gesproken deel duurt 4:26. De spreker praat in een natuurlijk tempo van ongeveer 158 woorden per minuut, dicht bij een alledaags gesprek. 85% van de woorden hoort bij de 3.000 meest gebruikte Engelse woorden; de rest kun je beter vooraf bekijken.
Belangrijke woorden in deze video
De 12 moeilijkste woorden uit de video, met uitspraak en betekenis:
| Woord | Uitspraak | Betekenis |
|---|---|---|
| algorithm zelfstandig naamwoord | /ˈælɡəɹɪðm̩/ | algoritme |
| excite werkwoord | /ɪkˈsaɪt/ | prikkelen, opwinden |
| augment werkwoord | /ɔɡˈmɛnt/ | vergroten, verhogen |
| dabble werkwoord | /ˈdæb.əl/ | dompelen, spetteren |
| diagnose werkwoord | /ˈdaɪ.əɡˌnoʊs/ | diagnosticeren |
| untapped bijvoeglijk naamwoord | onaangesproken | |
| terminology zelfstandig naamwoord | /ˌtɝ.məˈnɑ.lə.d͡ʒi/ | terminologie |
| worthwhile bijvoeglijk naamwoord | /ˌwɜː(ɹ)θˈwaɪl/ | de moeite lonend, de moeite waard |
| researcher zelfstandig naamwoord | /ˈɹiˌsɝ.t͡ʃɚ/ | onderzoeker |
| explicit bijvoeglijk naamwoord | /ɪkˈsplɪs.ɪt/ | expliciet, uitdrukkelijk |
| someday bijwoord | op een dag, ooit | |
| tremendous bijvoeglijk naamwoord | /tɹɪˈmɛndəs/ | ontzagwekkend |
Phrasal verbs die je zult horen
| Woord | Betekenis |
|---|---|
| grow up werkwoord | opgroeien |
| pick up werkwoord | oppakken, optillen |
| turn out werkwoord | uitvallen, resulteren |
Grammatica in deze video
De structuren die de spreker het meest gebruikt, met de exacte woorden uit de video:
| Structuur | In de video |
|---|---|
| Lijdende vorm be + voltooid deelwoord — het gaat om wat er gebeurt, niet om wie het doet | is sometimes called · has been overhyped · is estimated |
| Present perfect have/has + voltooid deelwoord — iets uit het verleden dat nu nog telt | you've learned · has been overhyped |
Uitspraak om op te letten
De spreker gebruikt 7 samentrekkingen en verkorte vormen, zoals we'll, don't, I've. Spreek ze kort uit, zoals je ze hoort.
- De “th”-klanken: algorithm /ˈælɡəɹɪðm̩/, worthwhile /ˌwɜː(ɹ)θˈwaɪl/
- Lange woorden — let op de klemtoon: terminology /ˌtɝ.məˈnɑ.lə.d͡ʒi/, automotive /ˌɔtəˈmoʊtɪv/
Klanken die Nederlandstaligen lastig vinden:
- /æ/ — opener dan de Nederlandse “e”: algorithm /ˈælɡəɹɪðm̩/, dabble /ˈdæb.əl/, vastly /ˈvæstli/
- /g/ — een harde plofklank, geen Nederlandse “g”: algorithm /ˈælɡəɹɪðm̩/, augment /ɔɡˈmɛnt/, diagnose /ˈdaɪ.əɡˌnoʊs/
Zo oefen je met deze video
- Luister de hele video één keer zonder te spreken en noteer de woorden die je niet kent.
- Begin op 0,75× snelheid, spreek zin voor zin na en ga terug naar normale snelheid zodra het makkelijk gaat.
- Neem jezelf op en vergelijk met het origineel; let daarbij op woorden als algorithm, excite, augment.
Wat is de Shadowing-techniek?
Shadowing is een wetenschappelijk onderbouwde taalleermethode die oorspronkelijk is ontwikkeld voor professionele tolkentraining en gepopulariseerd door polyglot Dr. Alexander Arguelles. De methode is eenvoudig maar krachtig: je luistert naar native Engelse audio en herhaalt het onmiddellijk hardop — als een schaduw die de spreker volgt met slechts 1–2 seconden vertraging. In tegenstelling tot passief luisteren of grammaticadrills, dwingt shadowing je hersenen en mondspieren om echte spraakpatronen tegelijkertijd te verwerken en te reproduceren. Onderzoek toont aan dat het de uitspraaknauwkeurigheid, intonatie, ritme, verbonden spraak, luisterbegrip en spreekvaardigheid aanzienlijk verbetert — waardoor het een van de meest effectieve methoden is voor IELTS Speaking-voorbereiding en echte Engelse communicatie.