शैडोइंग अभ्यास: Machine Learning | What Is Machine Learning? | Introduction To Machine Learning | 2026 | Simplilearn - वीडियो के साथ अंग्रेजी बोलना सीखें

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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✨ अनुशंसित वीडियो

64 वाक्य

इस पाठ की शब्दावली और बोलने के सुझाव

यह स्पीकिंग पाठ वीडियो “Machine Learning” पर आधारित है। सबसे ज़्यादा दोहराए गए शब्द ये हैं: learning, machine, song, model, paul। इस वीडियो में शैडोइंग के लिए 122 वाक्य और 1351 शब्द हैं। बोला गया हिस्सा 7:46 लंबा है। वक्ता लगभग 174 शब्द प्रति मिनट की स्वाभाविक गति से बोलता है, जो रोज़मर्रा की बातचीत के करीब है। केवल 80% शब्द अंग्रेज़ी के 3,000 सबसे आम शब्दों में से हैं, इसलिए शब्दावली कठिन है।

इस वीडियो की मुख्य शब्दावली

वीडियो के 15 सीखने लायक शब्द, उच्चारण और अर्थ के साथ:

शब्द	उच्चारण	अर्थ
coin संज्ञा	/ˈkoɪ̯n/	सिक्का, मुद्रा
label संज्ञा	/ˈleɪ.bəl/	लेबल
currency संज्ञा	/ˈkʌɹ.ən.si/	मुद्रा, करंसी
feedback संज्ञा	/ˈfiːdˌbæk/	प्रतिपुष्टि, प्रतिक्रिया
scenario संज्ञा	/sɪˈnɛəɹioʊ/	परिदृश्य
axis संज्ञा	/ˈæksɪs/	धुरी, अक्ष
cluster संज्ञा	/ˈklʌstɚ/	गुच्छ
dislike क्रिया	/dɪsˈlaɪk/	नापसंद करना
gram संज्ञा	/ˈɡɹæm/	ग्राम
algorithm संज्ञा	/ˈælɡəɹɪðm̩/	अल्गोरिद्म
weigh क्रिया	/weɪ/	तौलना, जोखना
complicated विशेषण	/ˈkɑm.plɪˌkeɪ.tɪd/	जटिल, पेचीदा
fraud संज्ञा	/ˈfɹɔːd/	धोखा, छल
remind क्रिया	/ɹəˈmaɪnd/	याद दिलाना
everyday विशेषण	/ˈɛvɹiˌdeɪ/	दैनिक, रोज़ाना

इस वीडियो का व्याकरण

वक्ता जिन संरचनाओं का सबसे अधिक प्रयोग करता है, वीडियो के असली शब्दों के साथ:

संरचना	वीडियो में
Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है	is associated · is unsupervised · was labeled
Present perfect have/has + past participle — बीता हुआ काम जिसका असर अभी भी है	you've understood supervised · has been updated
Relative clauses who / which + उपवाक्य — व्यक्ति या वस्तु के बारे में अतिरिक्त जानकारी	learning which is · those who need

ध्यान देने योग्य उच्चारण

वक्ता 4 बार संक्षिप्त रूपों का प्रयोग करता है, जैसे don't, I'll, I'm। इन्हें वैसे ही छोटा बोलें जैसा आप सुनते हैं।

“sh” और “zh” की ध्वनियाँ: transaction /tɹænˈzækʃən/, associate /əˈsoʊʃi.ət/, rush /ɹʌʃ/, genre /ˈ(d)ʒɑnɹə/, instruction /ɪnˈstɹʌkʃən/
लंबे शब्द — बलाघात सही रखें: intensity /ɪnˈtɛnsɪti/, complicated /ˈkɑm.plɪˌkeɪ.tɪd/, associate /əˈsoʊʃi.ət/, accuracy /ˈæk.jɚ.ə.si/, capability /ˌkeɪ.pəˈbɪl.ɪ.ti/

हिंदी भाषियों के लिए मुश्किल ध्वनियाँ:

/v/ और /w/ का फ़र्क़ — /v/ में ऊपर के दाँत निचले होंठ को छूते हैं, /w/ में होंठ गोल होते हैं: wicket /ˈwɪkɪt/, weigh /weɪ/, whereas /(h)wɛɹˈæz/, respective /ɹɪˈspɛktɪv/, everyday /ˈɛvɹiˌdeɪ/
/z/ — इसे “ज” (/dʒ/) न बनाएँ: transaction /tɹænˈzækʃən/, whereas /(h)wɛɹˈæz/, analyze /ˈæn.əˌlaɪz/, quiz /kwɪz/, minimize /ˈmɪn.ɪˌmaɪz/
/æ/ — “ऐ” से ज़्यादा खुला: feedback /ˈfiːdˌbæk/, axis /ˈæksɪs/, gram /ˈɡɹæm/, relax /ɹɪˈlæks/, transaction /tɹænˈzækʃən/

इस वीडियो से अभ्यास कैसे करें

पहले बिना बोले पूरा वीडियो एक बार सुनें और जो शब्द नहीं आते उन्हें लिख लें।
0.75× गति से शुरू करें, एक-एक वाक्य की शैडोइंग करें, और सहज लगने पर सामान्य गति पर लौटें।
अपनी आवाज़ रिकॉर्ड करें और मूल से तुलना करें, ख़ासकर coin, label, currency जैसे शब्दों पर ध्यान दें।

शैडोइंग तकनीक क्या है?

शैडोइंग (Shadowing) एक विज्ञान-समर्थित भाषा सीखने की तकनीक है जो मूल रूप से पेशेवर दुभाषिया प्रशिक्षण के लिए विकसित की गई थी। विधि सरल लेकिन शक्तिशाली है: आप मूल अंग्रेज़ी ऑडियो सुनते हैं और तुरंत इसे ज़ोर से दोहराते हैं — जैसे वक्ता की छाया 1-2 सेकंड की देरी से। शोध से पता चलता है कि यह उच्चारण सटीकता, स्वर, लय, जुड़ी हुई ध्वनियाँ, सुनने की समझ और बोलने की प्रवाहशीलता में काफ़ी सुधार करता है।

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इस पाठ की शब्दावली और बोलने के सुझाव

इस वीडियो की मुख्य शब्दावली

इस वीडियो का व्याकरण

ध्यान देने योग्य उच्चारण

इस वीडियो से अभ्यास कैसे करें

शैडोइंग तकनीक क्या है?