シャドーイング練習: A Gentle Introduction to Machine Learning - 動画で英語スピーキングを学ぶ
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Gonna start this StatQuest with a silly song.
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But if you don't like silly songs, that's okay.
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StatQuest.
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Hello, I'm Josh Starmer and welcome to StatQuest.
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Today we're going to do a gentle introduction to machine learning.
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Note, this StatQuest was originally prepared for and presented at the Society for Scientific Advancement's annual conference.
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One of the things that SOSA does is promote science and technology in Jamaica.
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Let's start with a silly example.
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Do you like silly songs?
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If you like silly songs, are you interested in machine learning?
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If you like silly songs and machine learning, then you'll love StatQuest.
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If you like silly songs but not machine learning, are you interested in statistics?
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If you like silly songs and statistics but not machine learning, then you'll still love StatQuest.
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Otherwise, you might not like StatQuest.
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If you don't like silly songs, are you interested in machine learning?
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If you don't like silly songs but you like machine learning, then you'll love StatQuest.
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If you don't like silly songs or machine learning, are you interested in statistics?
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If you don't like silly songs or machine learning, but you're interested in statistics, then you will love StatQuest.
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Otherwise, you might not like StatQuest.
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Wah wah.
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This is a silly example, but it illustrates a decision tree, a simple machine learning method.
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The purpose of this particular decision tree is to predict whether or not someone will love StatQuest.
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Alternatively, we could say that this decision tree classifies a person as either someone who loves StatQuest or someone who doesn't.
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Since decision trees are a type of machine learning, then if you understand how we use this tree to predict or classify if someone would love StatQuest,
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you are well on your way to understanding machine learning.
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Bam!
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Here's another silly example of machine learning.
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Imagine we measured how quickly someone could run 100 meters.
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And how much yam they ate.
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This is me.
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I'm not very fast, and I don't eat much yam.
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These are some other people.
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And this is Usain Bolt.
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Usain Bolt is very fast, and he eats a lot of yam.
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Given this pretend data, we see that the more yam someone eats, the faster they run the 100 meter dash.
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We can fit a black line to the data to show the trend.
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but we can also use the black line to make predictions.
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For example, if someone told us they ate this much yam, then we could use the black line to predict how fast that person might run.
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This is the predicted speed.
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The black line is a type of machine learning because we can use it to make predictions.
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In general, machine learning is all about making predictions and classifications.
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Bam!
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Now that we can make predictions and classifications, let's talk about some of the main ideas in machine learning.
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First of all, in machine learning lingo, the original data is called training data.
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So the black line is fit to training data.
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Alternatively, we could have fit a green squiggle to the training data.
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The green squiggle fits the training data better than the black line, but remember, the goal of machine learning is to make predictions.
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So we need a way to decide if the green squiggle is better or worse than the black line at making predictions.
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So we find a new person and measure how fast they run and how much yam they eat.
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And then we find another, and another, and another.
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All together, the blue dots represent testing data.
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We use the testing data to compare the predictions made by the black line to the predictions made by the green squiggle.
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Let's start by seeing how well the black line predicts the speed of each person in the testing data.
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Here's the first person in the testing data.
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They ate this much yam.
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And they ran this fast.
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However, the black line predicts that someone who ate this much yam should run a little slower.
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So let's measure the distance between the actual speed and the predicted speed,
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and save the distance on the right while we focus on the other people in the testing data.
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Here's the second person in the testing data.
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They ate this much yam, and they ran this fast.
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but the black line predicts that they will run a little faster.
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So we measure the distance between the actual speed and the predicted speed, and add it to the one we measured for the first person in the testing data.
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Then we measure the distance between the real and the predicted speed for the third person in the testing data,
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and add it to our running total of distances between the real and predicted speeds for the black line.
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Then we do the same thing for the fourth person in the testing data.
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And add that distance to our running total for the black line.
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This is the sum of all the distances between the real and predicted speeds for the black line.
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Now let's calculate the distances between the real and predicted speeds using the green squiggle.
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Remember, the green squiggle did a great job fitting the training data.
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But when we are doing machine learning, we are more interested in how well the green squiggle can make predictions with new data.
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So, just like before, we determine this person's real speed and their predicted speed and measure the distance between them.
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And just like we did for the black line, We'll keep track of the distances for the green squiggle over here.
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Then we do the same thing for the second person in the testing data. And the third person. And the fourth person.
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This is the sum of the distances between the real and predicted speeds for the green squiggle.
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The sum of the distances is larger for the green squiggle than the black line.
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In other words, even though the green squiggle fit the training data way better than the black line,
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the black line did a better job predicting speeds with the testing data.
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So if we had to choose between using the black line or the green squiggle to make predictions, we would choose the black line.
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Bam!
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This example teaches two main ideas about machine learning.
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First, we use testing data to evaluate machine learning methods.
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Second, don't be fooled by how well a machine learning method fits the training data.
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Fitting the training data well but making poor predictions is called the bias-variance trade-off.
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Oh no!
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A shameless self-promotion!
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If you want to learn more about the bias-variance trade-off, there's a stat quest that will walk you through it one step at a time.
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Before we move on, you may be wondering why we used a simple black line
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in a silly green squiggle instead of a Deep Learning Convolutional Neural Network.
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Or, Insert newest, bestest, most fancy machine learning method here.
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There are tons of fancy-sounding machine learning methods.
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And each year, something new and exciting comes on the scene.
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But regardless of what you use, the most important thing isn't how fancy it is, but how it performs with testing data.
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Double bam!
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Now let's go back to the decision tree that we started with.
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Remember, we wanted to classify if someone loves StatQuest based on a few questions.
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To create the decision tree, we collected data from people who love StatQuest, and from people who did not love StatQuest.
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Altogether, this was the training data, and we used it to build the decision tree.
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Then we got data from a few more people who love StatQuest, and a few more people who did not love StatQuest.
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Altogether, this forms the testing data.
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We can use the testing data to see how well our decision tree predicts if someone will love StatQuest.
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The first person in the testing data did not like silly songs, so we go to the right side of the decision tree.
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They didn't like machine learning, either.
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So we just keep on going down the right side of the decision tree.
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They didn't like statistics either.
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So the decision tree predicts that this person will not love StatQuest.
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However, this person loves StatQuest, so the decision tree made a mistake.
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Wah wah.
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The second person in the testing data liked silly songs.
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And that takes us down the left side of the decision tree.
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They were also interested in machine learning.
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So we predict that that person loves StatQuest.
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And since this person actually loves StatQuest, the decision tree did a good job.
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Hooray!
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Now we just run all of the other people in the testing data down the decision tree
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and compare the predictions to reality.
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Then we can compare this decision tree to the latest greatest machine learning method.
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Ultimately, we pick the method that does the best job predicting if someone will love StatQuest or not.
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Triple Bam!
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In summary, machine learning is all about making predictions and classifications.
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There are tons of fancy machine learning methods, but the most important thing to know about them isn't what makes them so fancy.
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It's that we decide which method fits our needs the best by using testing data.
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One last thing before we go.
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You may be wondering how we decide which data go into the training set and which data go into the testing set.
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Earlier, we just arbitrarily decided that these red dots were the training data.
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But the blue dots could have, just as easily, been the training data.
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The good news is that there are ways to determine
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which samples should be used for training data and which samples should be used for testing data.
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And if you're interested in learning more about this, check out the StatQuest.
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And there are lots more StatQuests that walk you through machine learning concepts step by step, so check them out.
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Hooray!
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We've made it to the end of another exciting StatQuest.
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If you like this StatQuest and want to see more, please subscribe.
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And if you want to support StatQuest, well, consider buying one or two of my original songs, or getting a t-shirt or a hoodie or some other slick merchandise.
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There's links on the screen and there's links in the description below.
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Alright, until next time, quest on!
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このレッスンについて
このレッスンでは、機械学習の基本的な概念に触れながら、英語のリスニングとスピーキングを練習します。特に、決定木のようなシンプルな機械学習の方法を用いて、情報を予測したり分類したりするプロセスを学びます。動画の内容を通じて、楽しい例を用いた英語の表現に慣れ、さまざまなトピックに関する話し方を向上させることができます。さらに、英語シャドーイング技術を活用することで、スムーズな発音やリズム感を身につけることが目指せます。
重要な語彙とフレーズ
- 機械学習 (Machine Learning)
- 決定木 (Decision Tree)
- トレーニングデータ (Training Data)
- 予測 (Prediction)
- 分類 (Classification)
- 速い (Fast)
- トレンド (Trend)
- データ (Data)
練習のヒント
動画のペースに合わせて英語シャドーイングを行う際には、まず少しずつセクションを分けて練習することをお勧めします。この動画は自然な会話のトーンで構成されているため、リズムとイントネーションにも注意を払いましょう。例えば、面白い例である「シリソング」の部分を強調して、その軽快さを真似してみてください。また、shadowspeaksのテクニックを使用して、声を重ねながら発音やリズムを身につけることができます。最初は難しいかもしれませんが、何回も繰り返すことでIELTS スピーキング対策にも役立ちます。YouTubeで英語学習を続ける中で、色々な例を通じて自分の言葉としてしっかりと再現できるようになりましょう。自信を持って話す力を育てていきましょう。
この動画の文法
話し手がよく使っている文型を、動画の実際の表現とともに紹介します。
| 文型 | 動画での表現 |
|---|---|
| 受動態 be + 過去分詞 — 誰がするかより、何が起きるかに焦点を当てる | is called · be fooled · should be used |
| 関係詞節 who / which + 節 — 人や物について情報を加える | someone who loves · someone who ate · people who love |
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。