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
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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.
このレッスンについて
「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!)】」を使って、シャドーイングで英語を練習しましょう。
毎日15〜30分の練習で、IELTSスピーキングへの自信と実践的な英会話力が身につきます。
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。