Prática de Shadowing: Machine Learning | What Is Machine Learning? | Introduction To Machine Learning | 2026 | Simplilearn - Aprenda a falar inglês com vídeo

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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29 frases

Vocabulário e dicas de fala para esta lição

Esta aula de conversação usa o vídeo “Machine Learning”. As palavras que mais se repetem: learning, machine, song, model, paul. Este vídeo tem 122 frases e 1351 palavras para praticar shadowing. A fala dura 7:46. O falante fala em ritmo natural, cerca de 174 palavras por minuto, próximo de uma conversa do dia a dia. Apenas 80% das palavras estão entre as 3.000 mais comuns do inglês, por isso o vocabulário é exigente.

Vocabulário principal deste vídeo

15 palavras do vídeo que vale a pena aprender, com pronúncia e significado:

Palavra	Pronúncia	Significado
coin substantivo	/ˈkoɪ̯n/	moeda, prata
label substantivo	/ˈleɪ.bəl/	etiqueta, rótulo
intensity substantivo	/ɪnˈtɛnsɪti/	intensidade
predict verbo	/pɹɪˈdɪkt/	prever
currency substantivo	/ˈkʌɹ.ən.si/	moeda
feedback substantivo	/ˈfiːdˌbæk/	comentário, avaliação
scenario substantivo	/sɪˈnɛəɹioʊ/	cenário
axis substantivo	/ˈæksɪs/	eixo
cluster substantivo	/ˈklʌstɚ/	aglomeração, aglomerado
dislike verbo	/dɪsˈlaɪk/	não gostar de, desgostar de
gram substantivo	/ˈɡɹæm/	grama
relax verbo	/ɹɪˈlæks/	soltar, afrouxar
transaction substantivo	/tɹænˈzækʃən/	transação
algorithm substantivo	/ˈælɡəɹɪðm̩/	algoritmo
weigh verbo	/weɪ/	pesar

Gramática neste vídeo

As estruturas que o falante mais usa, com as palavras exatas do vídeo:

Estrutura	No vídeo
Voz passiva be + particípio passado — o foco está no que acontece, não em quem faz	is associated · is unsupervised · was labeled
Present perfect have/has + particípio passado — uma ação passada que ainda importa agora	you've understood · has been updated
Orações relativas who / which + oração — informação extra sobre uma pessoa ou coisa	learning which is · those who need

Pronúncia para ficar de olho

O falante usa 4 contrações e formas reduzidas, como don't, I'll, I'm. Diga-as na forma curta, do jeito que você ouve.

Os sons de “sh” e “zh”: transaction /tɹænˈzækʃən/, associate /əˈsoʊʃi.ət/, rush /ɹʌʃ/, genre /ˈ(d)ʒɑnɹə/, instruction /ɪnˈstɹʌkʃən/
Palavras longas — acerte a sílaba tônica: intensity /ɪnˈtɛnsɪti/, complicated /ˈkɑm.plɪˌkeɪ.tɪd/, associate /əˈsoʊʃi.ət/, accuracy /ˈæk.jɚ.ə.si/, capability /ˌkeɪ.pəˈbɪl.ɪ.ti/

Sons difíceis para falantes de português:

Consoante final — sem acrescentar um “i” depois: predict /pɹɪˈdɪkt/, feedback /ˈfiːdˌbæk/, dislike /dɪsˈlaɪk/, wicket /ˈwɪkɪt/, complicated /ˈkɑm.plɪˌkeɪ.tɪd/
/l/ final — a língua toca o céu da boca, não vira “u”: label /ˈleɪ.bəl/, principle /ˈpɹɪn.sɪ.pəl/, differential [dɪfəˈɹənʃəɫ], computational /ˌkɑmpjʊˈteɪʃənəl/
/h/ — um sopro suave, diferente do “r”: whereas /(h)wɛɹˈæz/, hence /ˈhɛns/, humongous /ˌhjuːˈmʌŋɡəs/

Como praticar com este vídeo

Ouça o vídeo inteiro uma vez sem falar e anote as palavras que você não conhece.
Comece na velocidade 0,75×, faça shadowing frase por frase e volte à velocidade normal quando ficar fácil.
Grave a sua voz e compare com o original, prestando atenção a palavras como coin, label, intensity.

O que é a Técnica de Shadowing?

Shadowing é uma técnica de aprendizado de idiomas com base científica, originalmente desenvolvida para o treinamento de intérpretes profissionais. O método é simples, mas poderoso: você ouve áudio em inglês nativo e repete imediatamente em voz alta — como uma sombra seguindo o falante com 1-2 segundos de atraso. Pesquisas mostram melhora significativa na precisão da pronúncia, entonação, ritmo, sons conectados, compreensão auditiva e fluência na fala.

Técnica de shadowing: leia o guia completo passo a passo →

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Vocabulário e dicas de fala para esta lição

Vocabulário principal deste vídeo

Gramática neste vídeo

Pronúncia para ficar de olho

Como praticar com este vídeo

O que é a Técnica de Shadowing?