쉐도잉 연습: Machine Learning | What Is Machine Learning? | Introduction To Machine Learning | 2026 | Simplilearn - 영상으로 영어 말하기 배우기
레슨 만드는 중...
1
We know humans learn from their past experiences and machines follow instructions given by humans.
2
But what if humans can train the machines to learn from their past data
3
and do what humans can do and much faster?
4
Well, that's called machine learning.
5
But it's a lot more than just learning.
6
It's also about understanding and reasoning.
7
So today we will learn about the basics of machine learning.
8
So that's Paul.
9
He loves listening to new songs.
10
he either likes them
11
or dislikes them paul decides this on the basis of the song's tempo genre intensity
12
and the gender of voice for simplicity let's just use tempo
13
and intensity for now
14
so here tempo is on the x -axis ranging from relax
15
to fast whereas intensity is on the y -axis ranging from light to soaring we see
16
that paul likes the song with fast tempo and soaring intensity
17
while he dislikes the song with relaxed tempo and light intensity
18
so now we know Paul's choices let's say Paul listens to
19
a new song let's name it as song A song A has fast tempo
20
and a soaring intensity
21
so it lies somewhere here looking at the data can you guess whether Paul will like the song
22
or not correct
23
so Paul likes this song by looking at Paul's past choices
24
we were able to classify the unknown song very easily right
25
let's say now paul listens to a new song let's label it as song b
26
so song b lies somewhere here with medium tempo
27
and medium intensity neither relaxed nor fast neither light nor soaring now can you guess whether paul likes it
28
or not not able to guess whether paul will like it
29
or dislike it are the choices unclear correct we could easily classify song a
30
but when the choice became complicated as in the case of song B yes
31
and that's where machine learning comes in let's see how in the same example for song B
32
if we draw a circle around the song B we see
33
that there are four votes for like whereas one word for dislike
34
if we go for the majority votes we can say
35
that Paul will definitely like the song that's all this was
36
a basic machine learning algorithm also it's called k nearest neighbors
37
so So this is just a small example in one of the many machine learning algorithms.
38
Quite easy, right?
39
Believe me, it is.
40
But what happens when the choices become complicated, as in the case of Song B?
41
That's when machine learning comes in.
42
It learns the data, builds the prediction model, and when the new data point comes in, it can easily predict for it.
43
More the data, better the model, higher will be the accuracy.
44
There are many ways in which the machine learns.
45
It could be either supervised learning, unsupervised learning, or reinforcement learning.
46
Let's first quickly understand supervised learning.
47
Suppose your friend gives you 1 million coins of 3 different currencies, say 1 rupee, 1 euro, and 1 dirham.
48
Each coin has different weights.
49
For example, a coin of 1 rupee weighs 3 grams, 1 euro weighs 7 grams, and 1 dirham weighs 4 grams.
50
Your model will predict the currency of the coin.
51
Here, your weight becomes the feature of coins while currency becomes their label.
52
When you feed this data to the machine learning model, it learns which feature is associated with which label.
53
For example, it will learn that if a coin is of 3 grams, it will be a 1 rupee coin.
54
Let's give a new coin to the machine.
55
On the basis of the weight of the new coin, your model will predict the currency.
56
Hence, supervised learning uses labeled data to train the model.
57
Here, the machine knew the features of the object
58
and also the labels associated with those features on this note let's move to unsupervised learning
59
and see the difference suppose you have cricket data set of various players with their respective scores
60
and the wickets taken
61
when we feed this data set to the machine the machine identifies the pattern of player performance
62
so it plots this data with the respective wickets on the x -axis
63
while runs on the y -axis while looking at the data you will clearly see
64
that there are two clusters the one cluster are the players who scored high runs
65
and took less wickets while the other cluster is of the players who scored less runs
66
but took many wickets so here we interpret these two clusters as batsmen
67
and bowlers the important point to note here is
68
that there were no labels of batsmen
69
and bowlers hence the learning with unlabeled data is unsupervised learning
70
so we saw supervised learning where the data was labeled
71
and the unsupervised learning where the data was unlabeled
72
and then there is reinforcement learning which is a reward based learning
73
or we can say
74
that it works on the principle of feedback here let's say
75
you provide the system with an image of a dog
76
and ask it to identify it the system identifies it as a cat
77
so you give a negative feedback to the machine saying
78
that it's a dog's image the machine will learn from the feedback and finally
79
if it comes across any other image of a dog it will be able to classify it correctly
80
that is reinforced learning to generalize machine learning model let's see
81
a flowchart input is given to a machine learning model
82
which then gives the output according to the algorithm applied
83
if it's right we take the output as a final result else we provide feedback to the training model
84
and ask it to predict until it learns i hope you've understood supervised
85
and unsupervised learning
86
so let's have a quick quiz you have to determine whether the given scenarios uses supervised
87
or unsupervised learning simple right scenario 1 facebook recognizes your friend
88
in a picture from an album of tagged photographs scenario 2
89
netflix recommends new movies based on someone's past movie choices scenario 3 analyzing bank data for suspicious transactions
90
and flagging the fraud transactions think wisely
91
and comment below your answers moving on don't you sometimes wonder
92
how is machine learning possible in today's era well that's
93
because today we have humongous data available everybody's online either making a transaction
94
or just surfing the internet and that's generating a huge amount of data every minute
95
and that data my friend is the key to analysis also the memory handling capabilities of computers have largely increased
96
which helps them to process such huge amount of data at hand without any delay
97
and yes computers now have great computational powers
98
so there are a lot of applications of machine learning out
99
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!
📺 같은 채널
B22
What Is A Quality Management System (QMS) | Introduction To Quality Management System | Simplilearn
Business EnglishUniversity StudentsWorking Adults
B22
Digital Marketing In 5 Minutes | What Is Digital Marketing? | Learn Digital Marketing | Simplilearn
Business EnglishUniversity StudentsWorking Adults
B12
Top 5 Soft Skills For 2026 | Soft Skills Training | Most Important Skills To Learn | Simplilearn
Business EnglishInterview EnglishUniversity Students
B12
SEO In 5 Minutes | What Is SEO And How Does It Work | SEO Explained | SEO Tutorial | Simplilearn
Business EnglishWorking Adults
C13
Introduction To Software Development LifeCycle | What Is Software Development? | Simplilearn
Business EnglishWorking Adults
B21
What Are Your Strengths And Weaknesses? : Job Interview Questions And Answers | Simplilearn
Business EnglishInterview EnglishUniversity Students
C14
UI/UX Explained In 8 Minutes | UI/UX Design For Beginners | UI/UX Design Basics | Simplilearn
University Students
B2What Is CRM? | Introduction To CRM Software| CRM Projects For Beginners | CRM 2022 | Simplilearn
Business EnglishWorking Adults
✨ 추천 영상
C12
64 문장
P2 什么是机器学习【2024公认最好的 | 吴恩达机器学习 | 教程 | Machine Learning Specialization(超爽中英!)】
Business EnglishUniversity StudentsWorking Adults
C15
34 문장
Marketing Research Essentials Intro, Models & Intelligence
Business EnglishUniversity StudentsWorking Adults
B2152 문장
You're using Music Wrong in your Videos (here’s how to fix it)
Business EnglishUniversity StudentsWorking Adults
C12
58 문장
Natural Language Processing - Tokenization (NLP Zero to Hero - Part 1)
Business EnglishUniversity StudentsWorking Adults
C11
136 문장
A Gentle Introduction to Machine Learning
Business EnglishUniversity StudentsWorking Adults
C11
162 문장
Introduction to PLC | Siemens PLC Training Course
Business EnglishUniversity StudentsWorking Adults
C12
34 문장
Manufacturing Defined - What Is Manufacturing?
Business EnglishUniversity StudentsWorking Adults
C11
177 문장
AWS Machine Learning Engineer Associate: What You Need to Know
Business EnglishUniversity StudentsWorking Adults
C12
764 문장
11. Introduction to Machine Learning
Business EnglishUniversity StudentsWorking Adults
C15
107 문장
What is NLP? Neuro Linguistic Programming
Business EnglishUniversity StudentsWorking Adults
C18
55 문장
When AIs act emotional
Business EnglishUniversity StudentsWorking Adults
C18
114 문장
AI, Machine Learning, Deep Learning and Generative AI Explained
Business EnglishUniversity StudentsWorking Adults
이 레슨의 어휘와 말하기 포인트
이 말하기 레슨은 영상 “Machine Learning”을(를) 바탕으로 합니다. 가장 자주 반복되는 단어는 다음과 같습니다: learning, machine, song, model, paul. 이 영상에는 섀도잉할 문장 122개와 단어 1351개가 있습니다. 말하는 구간의 길이는 7:46입니다. 화자는 분당 약 174단어로, 일상 대화에 가까운 자연스러운 속도로 말합니다. 영어에서 가장 많이 쓰이는 3,000단어에 속하는 단어가 80%뿐이라 어휘가 어려운 편입니다.
이 영상의 핵심 어휘
영상에 나오는 익혀 둘 만한 단어 15개를 발음, 뜻과 함께 정리했습니다.
| 단어 | 발음 | 뜻 |
|---|---|---|
| coin 명사 | /ˈkoɪ̯n/ | 동전, 코인 |
| label 명사 | /ˈleɪ.bəl/ | 라벨, 레이블 |
| intensity 명사 | /ɪnˈtɛnsɪti/ | 강도 |
| predict 동사 | /pɹɪˈdɪkt/ | 예언하다, 예측하다 |
| currency 명사 | /ˈkʌɹ.ən.si/ | 통화, 화폐 |
| feedback 명사 | /ˈfiːdˌbæk/ | 피드백, 비평 |
| scenario 명사 | /sɪˈnɛəɹioʊ/ | 각본, 대본 |
| axis 명사 | /ˈæksɪs/ | 축 |
| dislike 동사 | /dɪsˈlaɪk/ | 싫어하다, 미워하다 |
| gram 명사 | /ˈɡɹæm/ | 그램 |
| relax 동사 | /ɹɪˈlæks/ | 늦추다, 편안히 하다 |
| transaction 명사 | /tɹænˈzækʃən/ | 처리 |
| algorithm 명사 | /ˈælɡəɹɪðm̩/ | 알고리즘, 알고리듬 |
| weigh 동사 | /weɪ/ | 달다 |
| complicated 형용사 | /ˈkɑm.plɪˌkeɪ.tɪd/ | 복잡하다 |
이 영상의 문법
화자가 가장 많이 쓰는 문형을 영상 속 실제 표현과 함께 정리했습니다.
| 문형 | 영상 속 표현 |
|---|---|
| 수동태 be + 과거분사 — 누가 하는지보다 무슨 일이 일어나는지에 초점 | is associated · is unsupervised · was labeled |
| 현재완료 have/has + 과거분사 — 과거의 일이 지금도 관련이 있을 때 | you've understood supervised · has been updated |
| 관계절 who / which + 절 — 사람이나 사물에 대한 추가 정보 | learning which is · those who need |
주의할 발음
화자는 don't, I'll, I'm 같은 축약형과 약화된 형태를 4번 사용합니다. 들리는 대로 짧게 발음하세요.
- “sh”와 “zh” 소리: transaction /tɹænˈzækʃən/, associate /əˈsoʊʃi.ət/, rush /ɹʌʃ/, genre /ˈ(d)ʒɑnɹə/, instruction /ɪnˈstɹʌkʃən/
- 긴 단어 — 강세 위치에 주의: intensity /ɪnˈtɛnsɪti/, complicated /ˈkɑm.plɪˌkeɪ.tɪd/, associate /əˈsoʊʃi.ət/, accuracy /ˈæk.jɚ.ə.si/, capability /ˌkeɪ.pəˈbɪl.ɪ.ti/
한국어 화자가 어려워하는 소리:
- /f/ — ㅍ(/p/)으로 바꾸지 말고 윗니를 아랫입술에 대기: feedback /ˈfiːdˌbæk/, fraud /ˈfɹɔːd/, photograph /ˈfoʊ.təˌɡɹæf/, fare /ˈfɛə/, surf /sɝf/
- /v/ — ㅂ(/b/)과 구별하기: respective /ɹɪˈspɛktɪv/, everyday /ˈɛvɹiˌdeɪ/, supervise /ˈsuː.pə.vaɪz/, predictive /pɹɪˈdɪk.tɪv/
- /z/ — ㅈ이 아니라 성대를 울리는 /s/: transaction /tɹænˈzækʃən/, whereas /(h)wɛɹˈæz/, analyze /ˈæn.əˌlaɪz/, quiz /kwɪz/, minimize /ˈmɪn.ɪˌmaɪz/
이 영상으로 연습하는 방법
- 먼저 말하지 않고 영상을 끝까지 듣고 모르는 단어를 적어 둡니다.
- 0.75배속으로 한 문장씩 섀도잉을 시작하고, 익숙해지면 보통 속도로 돌아갑니다.
- 자신의 목소리를 녹음해 원본과 비교하고, coin, label, intensity 같은 단어에 특히 주의합니다.
쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법
쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.