跟读练习: Machine Learning | What Is Machine Learning? | Introduction To Machine Learning | 2026 | Simplilearn - 通过视频学习英语口语
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We know humans learn from their past experiences and machines follow instructions given by humans.
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But what if humans can train the machines to learn from their past data
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and do what humans can do and much faster?
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Well, that's called machine learning.
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But it's a lot more than just learning.
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It's also about understanding and reasoning.
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So today we will learn about the basics of machine learning.
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So that's Paul.
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He loves listening to new songs.
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he either likes them
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or dislikes them paul decides this on the basis of the song's tempo genre intensity
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and the gender of voice for simplicity let's just use tempo
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and intensity for now
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so here tempo is on the x -axis ranging from relax
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to fast whereas intensity is on the y -axis ranging from light to soaring we see
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that paul likes the song with fast tempo and soaring intensity
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while he dislikes the song with relaxed tempo and light intensity
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so now we know Paul's choices let's say Paul listens to
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a new song let's name it as song A song A has fast tempo
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and a soaring intensity
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so it lies somewhere here looking at the data can you guess whether Paul will like the song
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or not correct
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so Paul likes this song by looking at Paul's past choices
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we were able to classify the unknown song very easily right
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let's say now paul listens to a new song let's label it as song b
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so song b lies somewhere here with medium tempo
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and medium intensity neither relaxed nor fast neither light nor soaring now can you guess whether paul likes it
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or not not able to guess whether paul will like it
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or dislike it are the choices unclear correct we could easily classify song a
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but when the choice became complicated as in the case of song B yes
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and that's where machine learning comes in let's see how in the same example for song B
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if we draw a circle around the song B we see
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that there are four votes for like whereas one word for dislike
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if we go for the majority votes we can say
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that Paul will definitely like the song that's all this was
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a basic machine learning algorithm also it's called k nearest neighbors
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so So this is just a small example in one of the many machine learning algorithms.
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Quite easy, right?
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Believe me, it is.
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But what happens when the choices become complicated, as in the case of Song B?
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That's when machine learning comes in.
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It learns the data, builds the prediction model, and when the new data point comes in, it can easily predict for it.
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More the data, better the model, higher will be the accuracy.
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There are many ways in which the machine learns.
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It could be either supervised learning, unsupervised learning, or reinforcement learning.
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Let's first quickly understand supervised learning.
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Suppose your friend gives you 1 million coins of 3 different currencies, say 1 rupee, 1 euro, and 1 dirham.
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Each coin has different weights.
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For example, a coin of 1 rupee weighs 3 grams, 1 euro weighs 7 grams, and 1 dirham weighs 4 grams.
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Your model will predict the currency of the coin.
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Here, your weight becomes the feature of coins while currency becomes their label.
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When you feed this data to the machine learning model, it learns which feature is associated with which label.
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For example, it will learn that if a coin is of 3 grams, it will be a 1 rupee coin.
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Let's give a new coin to the machine.
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On the basis of the weight of the new coin, your model will predict the currency.
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Hence, supervised learning uses labeled data to train the model.
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Here, the machine knew the features of the object
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and also the labels associated with those features on this note let's move to unsupervised learning
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and see the difference suppose you have cricket data set of various players with their respective scores
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and the wickets taken
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when we feed this data set to the machine the machine identifies the pattern of player performance
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so it plots this data with the respective wickets on the x -axis
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while runs on the y -axis while looking at the data you will clearly see
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that there are two clusters the one cluster are the players who scored high runs
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and took less wickets while the other cluster is of the players who scored less runs
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but took many wickets so here we interpret these two clusters as batsmen
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and bowlers the important point to note here is
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that there were no labels of batsmen
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and bowlers hence the learning with unlabeled data is unsupervised learning
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so we saw supervised learning where the data was labeled
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and the unsupervised learning where the data was unlabeled
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and then there is reinforcement learning which is a reward based learning
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or we can say
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that it works on the principle of feedback here let's say
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you provide the system with an image of a dog
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and ask it to identify it the system identifies it as a cat
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so you give a negative feedback to the machine saying
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that it's a dog's image the machine will learn from the feedback and finally
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if it comes across any other image of a dog it will be able to classify it correctly
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that is reinforced learning to generalize machine learning model let's see
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a flowchart input is given to a machine learning model
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which then gives the output according to the algorithm applied
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if it's right we take the output as a final result else we provide feedback to the training model
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and ask it to predict until it learns i hope you've understood supervised
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and unsupervised learning
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so let's have a quick quiz you have to determine whether the given scenarios uses supervised
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or unsupervised learning simple right scenario 1 facebook recognizes your friend
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in a picture from an album of tagged photographs scenario 2
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netflix recommends new movies based on someone's past movie choices scenario 3 analyzing bank data for suspicious transactions
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and flagging the fraud transactions think wisely
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and comment below your answers moving on don't you sometimes wonder
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how is machine learning possible in today's era well that's
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because today we have humongous data available everybody's online either making a transaction
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or just surfing the internet and that's generating a huge amount of data every minute
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and that data my friend is the key to analysis also the memory handling capabilities of computers have largely increased
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which helps them to process such huge amount of data at hand without any delay
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and yes computers now have great computational powers
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so there are a lot of applications of machine learning out
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there to name a few machine learning is used in healthcare
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where diagnostics are predicted for doctor's review the sentiment analysis
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that the tech giants are doing on social media is another
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interesting application of machine learning fraud detection in the finance sector
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and also to predict customer churn in the e -commerce sector
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while booking a gap you must have encountered surge pricing often
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where it says the fare of your trip has been updated
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continue booking yes please i'm getting late for office well that's an interesting machine learning model
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which is used by global taxi giant uber
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and others where they have differential pricing in real time based
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on demand the number of cars available bad weather rush hour etc
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so they use the surge pricing model to ensure
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that those who need a cab can get one also it
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uses predictive of modeling to predict where the demand will be high with a goal
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that drivers can take care of the demand and search pricing can be minimized.
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Great!
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Hey Siri, can you remind me to book a cab at 6pm today?
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Okay, I'll remind you.
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Thanks.
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No problem.
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Comment below some interesting everyday examples around you where machines are learning and doing amazing jobs.
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So that's all for machine learning basics today from my side.
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Keep watching this space for more interesting videos.
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Until then, happy learning!
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本课的词汇与口语要点
这节口语课以视频“Machine Learning”为素材。 视频中反复出现的词有：learning, machine, song, model, paul。 这段视频共有 122 个句子、1351 个单词可供跟读。 讲话部分时长为 7:46。 说话人语速自然，每分钟约 174 个词，接近日常对话。 只有 80% 的单词属于英语最常用的 3,000 词，词汇难度较高。
视频中的重点词汇
视频中 15 个值得学习的单词，附发音和释义：
| 单词 | 发音 | 释义 |
|---|---|---|
| coin 名词 | /ˈkoɪ̯n/ | 硬幣 /硬币, 銅錢 /铜钱 |
| label 名词 | /ˈleɪ.bəl/ | 標籤 /标签, 簽條 /签条 |
| intensity 名词 | /ɪnˈtɛnsɪti/ | 強度 /强度, 強烈 /强烈 |
| predict 动词 | /pɹɪˈdɪkt/ | 預言 /预言 |
| currency 名词 | /ˈkʌɹ.ən.si/ | 貨幣 /货币 |
| feedback 名词 | /ˈfiːdˌbæk/ | 反饋 /反馈, 反應 /反应 |
| scenario 名词 | /sɪˈnɛəɹioʊ/ | 腳本 /脚本, 場景 /场景 |
| axis 名词 | /ˈæksɪs/ | 軸 /轴, 軸心 /轴心 |
| cluster 名词 | /ˈklʌstɚ/ | 串, 叢 /丛 |
| dislike 动词 | /dɪsˈlaɪk/ | 不喜愛 /不喜爱, 不喜歡 /不喜欢 |
| gram 名词 | /ˈɡɹæm/ | 克, 公克 |
| relax 动词 | /ɹɪˈlæks/ | 鬆開 /松开, 放鬆 /放松 |
| transaction 名词 | /tɹænˈzækʃən/ | 處理 /处理 |
| algorithm 名词 | /ˈælɡəɹɪðm̩/ | 算法, 演算法 |
| weigh 动词 | /weɪ/ | 稱重 /称重 |
视频中的语法
说话人最常用的结构，并附上视频中的原话：
| 结构 | 视频中的用法 |
|---|---|
| 被动语态 be + 过去分词 — 强调发生了什么，而不是谁做的 | is associated · is unsupervised · was labeled |
| 现在完成时 have/has + 过去分词 — 过去发生但与现在仍有关联的事 | you've understood supervised · has been updated |
| 定语从句 who / which + 从句 — 补充说明人或事物 | learning which is · those who need |
需要注意的发音
说话人用了 4 次缩略和弱读形式，例如 don't, I'll, I'm。请按听到的简短形式来说。
- “sh” 和 “zh” 音: transaction /tɹænˈzækʃən/, associate /əˈsoʊʃi.ət/, rush /ɹʌʃ/, genre /ˈ(d)ʒɑnɹə/, instruction /ɪnˈstɹʌkʃən/
- 长单词——注意重音位置: intensity /ɪnˈtɛnsɪti/, complicated /ˈkɑm.plɪˌkeɪ.tɪd/, associate /əˈsoʊʃi.ət/, accuracy /ˈæk.jɚ.ə.si/, capability /ˌkeɪ.pəˈbɪl.ɪ.ti/
中文母语者容易读错的音：
- /v/ — 不要读成 /w/，上齿轻触下唇: respective /ɹɪˈspɛktɪv/, everyday /ˈɛvɹiˌdeɪ/, supervise /ˈsuː.pə.vaɪz/, predictive /pɹɪˈdɪk.tɪv/
- 词尾辅音 — 要读清楚，后面不要加元音: predict /pɹɪˈdɪkt/, feedback /ˈfiːdˌbæk/, axis /ˈæksɪs/, dislike /dɪsˈlaɪk/, wicket /ˈwɪkɪt/
- /r/ 和 /l/ 的区别: relax /ɹɪˈlæks/, algorithm /ˈælɡəɹɪðm̩/, principle /ˈpɹɪn.sɪ.pəl/, correctly /kəˈɹɛk(t).li/, differential [dɪfəˈɹənʃəɫ]
如何用这段视频练习
- 先完整听一遍视频，不要开口，记下不认识的单词。
- 先用 0.75 倍速逐句跟读，熟练之后再回到正常速度。
- 录下自己的声音并与原声对比，特别注意 coin, label, intensity 这类单词。
什么是跟读法？
跟读法 (Shadowing) 是一种有科学依据的语言学习技巧，最初开发用于专业口译员的培训，并由多语言者Alexander Arguelles博士普及。这个方法简单而强大：您在听英语母语原声的同时立即大声重复——就像是一个延迟1-2秒紧跟说话者的影子。与被动听力或语法练习不同，跟读法强迫您的大脑和口腔肌肉同时处理并模仿真实的讲话模式。研究表明它能显着提高发音准确性，语调，节奏，连读，听力理解和口语流利度——使其成为雅思口语备考和真实英语交流最有效的方法之一。