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
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1
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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Vocabulary and speaking notes for this lesson
This C1 speaking lesson is built on the video “P1 机器学习的应用【2024公认最好的”. The speaker keeps coming back to these words: machine, learning, algorithms, applications, learn. This video has 43 sentences and 698 words to shadow. The speech runs for 4:26. The speaker talks at a natural 158 words per minute, close to everyday conversation. 85% of the words are among the 3,000 most common in English; the rest is worth studying before you start.
Key vocabulary in this video
The 15 most advanced words in the video, with pronunciation and meaning:
| Word | Pronunciation | Meaning |
|---|---|---|
| algorithm noun | /ˈælɡəɹɪðm̩/ | A collection of ordered steps that solve a mathematical problem. A precise step-by-step plan for a computational procedure that possibly begins with an input… |
| excite verb | /ɪkˈsaɪt/ | To stir the emotions of; to cause to feel excitement. |
| augment verb | /ɔɡˈmɛnt/ | To increase; to make larger or supplement. |
| dabble verb | /ˈdæb.əl/ | To make slightly wet or soiled by spattering or sprinkling a liquid (such as water, mud, or paint) on it; to bedabble. |
| diagnose verb | /ˈdaɪ.əɡˌnoʊs/ | To determine which disease is causing a sick person's signs and symptoms; to find the diagnosis. |
| overhype verb | To promote or publicize excessively. | |
| subfield noun | A smaller, more specialized area of study or occupation within a larger one | |
| unfulfilled adjective | Lacking fulfillment; marked by a feeling of failure to achieve goals or desires. | |
| untapped adjective | Not tapped; not drawn on in terms of resources. | |
| vastly adverb | /ˈvæstli/ | Greatly, in a vast manner. |
| terminology noun | /ˌtɝ.məˈnɑ.lə.d͡ʒi/ | A treatise on terms, especially those used in a specialised field. |
| worthwhile adjective | /ˌwɜː(ɹ)θˈwaɪl/ | Good and important enough to spend time, effort, or money on. |
| researcher noun | /ˈɹiˌsɝ.t͡ʃɚ/ | One who researches. |
| automotive adjective | /ˌɔtəˈmoʊtɪv/ | Having the ability to move by itself; self-propelled or self-propelling. |
| explicit adjective | /ɪkˈsplɪs.ɪt/ | Very specific, clear, or detailed. |
Phrasal verbs you will hear
| Word | Meaning |
|---|---|
| grow up verb | To mature and become an adult. |
| pick up verb | To lift; to grasp and raise. |
| turn out verb | To end up; to result. |
דקדוק בסרטון הזה
המבנים שהדובר משתמש בהם הכי הרבה, עם המילים המדויקות מהסרטון:
| מבנה | בסרטון |
|---|---|
| סביל (Passive voice) be + past participle — הדגש הוא על מה שקורה, לא על מי שעושה את הפעולה | is sometimes called · has been overhyped · is estimated |
| הווה מושלם (Present perfect) have/has + past participle — פעולה שקרתה בעבר ועדיין יש לה חשיבות או השפעה עכשיו | you've learned · has been overhyped |
Pronunciation to watch
The speaker uses 7 contractions and reduced forms, such as we'll, don't, I've. Say them the short way, as you hear them.
- The “th” sounds: algorithm /ˈælɡəɹɪðm̩/, worthwhile /ˌwɜː(ɹ)θˈwaɪl/
- Long words — get the stress right: terminology /ˌtɝ.məˈnɑ.lə.d͡ʒi/, automotive /ˌɔtəˈmoʊtɪv/
How to practise with this video
- Listen to the whole video once without speaking and note the words you do not know.
- Start at 0.75× speed, shadow it sentence by sentence, then go back to normal speed once it feels easy.
- Record yourself and compare with the original, paying attention to words like algorithm, excite, augment.
מהי טכניקת השאדואינג?
שאדואינג היא טכניקת למידת שפה מבוססת מדע שפותחה במקור להכשרת מתורגמנים מקצועיים ופופולרית על ידי הפוליגלוט ד"ר אלכסנדר ארגואלס. השיטה פשוטה אך עוצמתית: אתה מקשיב לאודיו באנגלית ילידית וחוזר עליו בקול רם מיד — כמו צל העוקב אחרי הדובר עם עיכוב של 1–2 שניות בלבד. בניגוד להאזנה פסיבית או תרגילי דקדוק, שאדואינג מכריח את המוח והשרירים בפה לעבד ולשחזר דפוסי דיבור אמיתיים בו זמנית. מחקרים מראים שזה משפר משמעותית את דיוק ההגייה, האינטונציה, הקצב, הדיבור המחובר, הבנת השמיעה ושטף הדיבור — מה שהופך אותו לאחת השיטות היעילות ביותר להכנה לIELTS Speaking ולתקשורת באנגלית בעולם האמיתי.