Luyện nói tiếng Anh bằng Shadowing qua video: Machine Learning | What Is Machine Learning? | Introduction To Machine Learning | 2026 | Simplilearn

We know humans learn from their past experiences and machines follow instructions given by humans.

But what if humans can train the machines to learn from their past data

and do what humans can do and much faster?

Well, that's called machine learning.

But it's a lot more than just learning.

It's also about understanding and reasoning.

So today we will learn about the basics of machine learning.

So that's Paul.

He loves listening to new songs.

he either likes them

or dislikes them paul decides this on the basis of the song's tempo genre intensity

and the gender of voice for simplicity let's just use tempo

and intensity for now

so here tempo is on the x -axis ranging from relax

to fast whereas intensity is on the y -axis ranging from light to soaring we see

that paul likes the song with fast tempo and soaring intensity

while he dislikes the song with relaxed tempo and light intensity

so now we know Paul's choices let's say Paul listens to

a new song let's name it as song A song A has fast tempo

and a soaring intensity

so it lies somewhere here looking at the data can you guess whether Paul will like the song

or not correct

so Paul likes this song by looking at Paul's past choices

we were able to classify the unknown song very easily right

let's say now paul listens to a new song let's label it as song b

so song b lies somewhere here with medium tempo

and medium intensity neither relaxed nor fast neither light nor soaring now can you guess whether paul likes it

or not not able to guess whether paul will like it

or dislike it are the choices unclear correct we could easily classify song a

but when the choice became complicated as in the case of song B yes

and that's where machine learning comes in let's see how in the same example for song B

if we draw a circle around the song B we see

that there are four votes for like whereas one word for dislike

if we go for the majority votes we can say

that Paul will definitely like the song that's all this was

a basic machine learning algorithm also it's called k nearest neighbors

so So this is just a small example in one of the many machine learning algorithms.

Quite easy, right?

Believe me, it is.

But what happens when the choices become complicated, as in the case of Song B?

That's when machine learning comes in.

It learns the data, builds the prediction model, and when the new data point comes in, it can easily predict for it.

More the data, better the model, higher will be the accuracy.

There are many ways in which the machine learns.

It could be either supervised learning, unsupervised learning, or reinforcement learning.

Let's first quickly understand supervised learning.

Suppose your friend gives you 1 million coins of 3 different currencies, say 1 rupee, 1 euro, and 1 dirham.

Each coin has different weights.

For example, a coin of 1 rupee weighs 3 grams, 1 euro weighs 7 grams, and 1 dirham weighs 4 grams.

Your model will predict the currency of the coin.

Here, your weight becomes the feature of coins while currency becomes their label.

When you feed this data to the machine learning model, it learns which feature is associated with which label.

For example, it will learn that if a coin is of 3 grams, it will be a 1 rupee coin.

Let's give a new coin to the machine.

On the basis of the weight of the new coin, your model will predict the currency.

Hence, supervised learning uses labeled data to train the model.

Here, the machine knew the features of the object

and also the labels associated with those features on this note let's move to unsupervised learning

and see the difference suppose you have cricket data set of various players with their respective scores

and the wickets taken

when we feed this data set to the machine the machine identifies the pattern of player performance

so it plots this data with the respective wickets on the x -axis

while runs on the y -axis while looking at the data you will clearly see

that there are two clusters the one cluster are the players who scored high runs

and took less wickets while the other cluster is of the players who scored less runs

but took many wickets so here we interpret these two clusters as batsmen

and bowlers the important point to note here is

that there were no labels of batsmen

and bowlers hence the learning with unlabeled data is unsupervised learning

so we saw supervised learning where the data was labeled

and the unsupervised learning where the data was unlabeled

and then there is reinforcement learning which is a reward based learning

or we can say

that it works on the principle of feedback here let's say

you provide the system with an image of a dog

and ask it to identify it the system identifies it as a cat

so you give a negative feedback to the machine saying

that it's a dog's image the machine will learn from the feedback and finally

if it comes across any other image of a dog it will be able to classify it correctly

that is reinforced learning to generalize machine learning model let's see

a flowchart input is given to a machine learning model

which then gives the output according to the algorithm applied

if it's right we take the output as a final result else we provide feedback to the training model

and ask it to predict until it learns i hope you've understood supervised

and unsupervised learning

so let's have a quick quiz you have to determine whether the given scenarios uses supervised

or unsupervised learning simple right scenario 1 facebook recognizes your friend

in a picture from an album of tagged photographs scenario 2

netflix recommends new movies based on someone's past movie choices scenario 3 analyzing bank data for suspicious transactions

and flagging the fraud transactions think wisely

and comment below your answers moving on don't you sometimes wonder

how is machine learning possible in today's era well that's

because today we have humongous data available everybody's online either making a transaction

or just surfing the internet and that's generating a huge amount of data every minute

and that data my friend is the key to analysis also the memory handling capabilities of computers have largely increased

which helps them to process such huge amount of data at hand without any delay

and yes computers now have great computational powers

so there are a lot of applications of machine learning out

there to name a few machine learning is used in healthcare

100

where diagnostics are predicted for doctor's review the sentiment analysis

101

that the tech giants are doing on social media is another

102

interesting application of machine learning fraud detection in the finance sector

103

and also to predict customer churn in the e -commerce sector

104

while booking a gap you must have encountered surge pricing often

105

where it says the fare of your trip has been updated

106

continue booking yes please i'm getting late for office well that's an interesting machine learning model

107

which is used by global taxi giant uber

108

and others where they have differential pricing in real time based

109

on demand the number of cars available bad weather rush hour etc

110

so they use the surge pricing model to ensure

111

that those who need a cab can get one also it

112

uses predictive of modeling to predict where the demand will be high with a goal

113

that drivers can take care of the demand and search pricing can be minimized.

114

Great!

115

Hey Siri, can you remind me to book a cab at 6pm today?

116

Okay, I'll remind you.

117

Thanks.

118

No problem.

119

Comment below some interesting everyday examples around you where machines are learning and doing amazing jobs.

120

So that's all for machine learning basics today from my side.

121

Keep watching this space for more interesting videos.

122

Until then, happy learning!

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✨ Video gợi ý

64 câu

Từ vựng và ghi chú luyện nói cho bài học này

Bài luyện nói này dựa trên video “Machine Learning”. Những từ được nhắc lại nhiều nhất trong bài: learning, machine, song, model, paul. Video này có 122 câu và 1351 từ để luyện shadowing. Phần lời nói dài 7:46. Người nói nói với tốc độ tự nhiên, khoảng 174 từ mỗi phút, gần với hội thoại hằng ngày. Chỉ 80% số từ nằm trong 3.000 từ tiếng Anh thông dụng nhất, nên từ vựng khá khó.

Từ vựng quan trọng trong video

15 từ đáng học trong video, kèm phiên âm và nghĩa:

Từ	Phiên âm	Nghĩa
coin danh từ	/ˈkoɪ̯n/	đồng tiền
label danh từ	/ˈleɪ.bəl/	nhãn
predict động từ	/pɹɪˈdɪkt/	dự báo
currency danh từ	/ˈkʌɹ.ən.si/	tiền tệ, ngoại tệ
feedback danh từ	/ˈfiːdˌbæk/	sự phản hồi
scenario danh từ	/sɪˈnɛəɹioʊ/	trường hợp, kịch bản
axis danh từ	/ˈæksɪs/	trục
dislike động từ	/dɪsˈlaɪk/	không ưa, không thích
gram danh từ	/ˈɡɹæm/	gam
algorithm danh từ	/ˈælɡəɹɪðm̩/	thuật toán
weigh động từ	/weɪ/	cân, đo
complicated tính từ	/ˈkɑm.plɪˌkeɪ.tɪd/	phức tạp
output danh từ	/ˈaʊtpʊt/	đầu ra
fraud danh từ	/ˈfɹɔːd/	sự gian lận, sự gian trá
remind động từ	/ɹəˈmaɪnd/	nhắc nhở, nhắc

Ngữ pháp trong video

Những cấu trúc người nói dùng nhiều nhất, kèm đúng cụm từ trong video:

Cấu trúc	Trong video
Câu bị động be + quá khứ phân từ — nhấn vào việc xảy ra, không phải người làm	is associated · is unsupervised · was labeled
Thì hiện tại hoàn thành have/has + quá khứ phân từ — việc đã xảy ra nhưng còn liên quan đến hiện tại	you've understood supervised · has been updated
Mệnh đề quan hệ who / which + mệnh đề — thêm thông tin về người hoặc vật	learning which is · those who need

Phát âm cần chú ý

Người nói dùng 4 dạng rút gọn, ví dụ don't, I'll, I'm. Hãy nói theo dạng ngắn đúng như bạn nghe.

Âm “sh” và “zh”: transaction /tɹænˈzækʃən/, associate /əˈsoʊʃi.ət/, rush /ɹʌʃ/, genre /ˈ(d)ʒɑnɹə/, instruction /ɪnˈstɹʌkʃən/
Từ dài — đặt trọng âm cho đúng: intensity /ɪnˈtɛnsɪti/, complicated /ˈkɑm.plɪˌkeɪ.tɪd/, associate /əˈsoʊʃi.ət/, accuracy /ˈæk.jɚ.ə.si/, capability /ˌkeɪ.pəˈbɪl.ɪ.ti/

Những âm người Việt thường gặp khó:

Cụm phụ âm cuối — đọc đủ từng âm, đừng nuốt âm cuối: predict /pɹɪˈdɪkt/, relax /ɹɪˈlæks/, hence /ˈhɛns/, remind /ɹəˈmaɪnd/, reward /ɹɪˈwɔɹd/
Âm /tʃ/, /dʒ/ và /ʒ/ — tiếng Việt không có, đừng đọc thành “ch” hay “gi”: surge /sɝd͡ʒ/, generate /ˈd͡ʒɛn.ə.ɹeɪt/, genre /ˈ(d)ʒɑnɹə/, churn /t͡ʃɝn/, generalize /ˈd͡ʒɛn.(ə.)ɹə.laɪz/
Âm /æ/ — mở miệng rộng hơn âm “e”: feedback /ˈfiːdˌbæk/, axis /ˈæksɪs/, gram /ˈɡɹæm/, relax /ɹɪˈlæks/, transaction /tɹænˈzækʃən/

Cách luyện với video này

Nghe hết video một lần, chưa cần nói, và ghi lại những từ bạn chưa biết.
Bắt đầu ở tốc độ 0,75×, nói đuổi từng câu, rồi quay lại tốc độ bình thường khi đã quen.
Ghi âm giọng mình rồi so với bản gốc, chú ý các từ như coin, label, predict.

Phương Pháp Shadowing Là Gì?

Shadowing là kỹ thuật học ngôn ngữ có cơ sở khoa học, ban đầu được phát triển cho chương trình đào tạo phiên dịch viên chuyên nghiệp và được phổ biến rộng rãi bởi nhà đa ngôn ngữ học Dr. Alexander Arguelles. Nguyên lý cốt lõi đơn giản nhưng cực kỳ hiệu quả: bạn nghe tiếng Anh của người bản xứ và lặp lại to ngay lập tức — như một "cái bóng" (shadow) đuổi theo người nói với độ trễ chỉ 1–2 giây. Khác với luyện ngữ pháp hay học từ vựng bị động, Shadowing buộc não bộ và cơ miệng phải đồng thời xử lý và tái tạo ngôn ngữ thực tế. Các nghiên cứu khoa học xác nhận phương pháp này cải thiện đáng kể phát âm, ngữ điệu, nhịp điệu, nối âm, kỹ năng nghe và độ lưu loát khi nói — đặc biệt hiệu quả cho người luyện IELTS Speaking và muốn giao tiếp tiếng Anh tự nhiên như người bản ngữ.

Phương pháp shadowing: đọc hướng dẫn từng bước đầy đủ →

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Từ vựng và ghi chú luyện nói cho bài học này

Từ vựng quan trọng trong video

Ngữ pháp trong video

Phát âm cần chú ý

Cách luyện với video này

Phương Pháp Shadowing Là Gì?