Shadowing Practice: Machine Learning | What Is Machine Learning? | Introduction To Machine Learning | 2026 | Simplilearn - Learn English Speaking with Video
Les maken...
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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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Woordenschat en spreektips bij deze les
Deze spreekles is gebaseerd op de video “Machine Learning”. Deze woorden komen het vaakst terug: learning, machine, song, model, paul. Deze video bevat 122 zinnen en 1351 woorden om na te spreken. Het gesproken deel duurt 7:46. De spreker praat in een natuurlijk tempo van ongeveer 174 woorden per minuut, dicht bij een alledaags gesprek. Slechts 80% van de woorden hoort bij de 3.000 meest gebruikte Engelse woorden, dus de woordenschat is pittig.
Belangrijke woorden in deze video
15 woorden uit de video die het leren waard zijn, met uitspraak en betekenis:
| Woord | Uitspraak | Betekenis |
|---|---|---|
| coin zelfstandig naamwoord | /ˈkoɪ̯n/ | munt, muntstuk |
| label zelfstandig naamwoord | /ˈleɪ.bəl/ | etiket, label |
| predict werkwoord | /pɹɪˈdɪkt/ | voorspellen |
| currency zelfstandig naamwoord | /ˈkʌɹ.ən.si/ | munteenheid, valuta |
| feedback zelfstandig naamwoord | /ˈfiːdˌbæk/ | terugkoppeling |
| scenario zelfstandig naamwoord | /sɪˈnɛəɹioʊ/ | scenario |
| axis zelfstandig naamwoord | /ˈæksɪs/ | as |
| cluster zelfstandig naamwoord | /ˈklʌstɚ/ | tros |
| dislike werkwoord | /dɪsˈlaɪk/ | een hekel hebben aan, het land hebben aan |
| gram zelfstandig naamwoord | /ˈɡɹæm/ | gram |
| relax werkwoord | /ɹɪˈlæks/ | losmaken |
| algorithm zelfstandig naamwoord | /ˈælɡəɹɪðm̩/ | algoritme |
| weigh werkwoord | /weɪ/ | wegen |
| complicated bijvoeglijk naamwoord | /ˈkɑm.plɪˌkeɪ.tɪd/ | ingewikkeld, gecompliceerd |
| output zelfstandig naamwoord | /ˈaʊtpʊt/ | productie |
Phrasal verbs die je zult horen
| Woord | Betekenis |
|---|---|
| come across werkwoord | overkomen |
Grammatica in deze video
De structuren die de spreker het meest gebruikt, met de exacte woorden uit de video:
| Structuur | In de video |
|---|---|
| Lijdende vorm be + voltooid deelwoord — het gaat om wat er gebeurt, niet om wie het doet | is associated · is unsupervised · was labeled |
| Present perfect have/has + voltooid deelwoord — iets uit het verleden dat nu nog telt | you've understood supervised · has been updated |
| Betrekkelijke bijzinnen who / which + zin — extra informatie over een persoon of ding | learning which is · those who need |
Uitspraak om op te letten
De spreker gebruikt 4 samentrekkingen en verkorte vormen, zoals don't, I'll, I'm. Spreek ze kort uit, zoals je ze hoort.
- De klanken “sh” en “zh”: transaction /tɹænˈzækʃən/, associate /əˈsoʊʃi.ət/, rush /ɹʌʃ/, genre /ˈ(d)ʒɑnɹə/, instruction /ɪnˈstɹʌkʃən/
- Lange woorden — let op de klemtoon: intensity /ɪnˈtɛnsɪti/, complicated /ˈkɑm.plɪˌkeɪ.tɪd/, associate /əˈsoʊʃi.ət/, accuracy /ˈæk.jɚ.ə.si/, capability /ˌkeɪ.pəˈbɪl.ɪ.ti/
Klanken die Nederlandstaligen lastig vinden:
- Stemhebbende eindmedeklinker — /b/, /d/, /g/, /z/, /v/ niet verscherpen: complicated /ˈkɑm.plɪˌkeɪ.tɪd/, whereas /(h)wɛɹˈæz/, fraud /ˈfɹɔːd/, remind /ɹəˈmaɪnd/, respective /ɹɪˈspɛktɪv/
- /æ/ — opener dan de Nederlandse “e”: feedback /ˈfiːdˌbæk/, axis /ˈæksɪs/, gram /ˈɡɹæm/, relax /ɹɪˈlæks/, transaction /tɹænˈzækʃən/
- /g/ — een harde plofklank, geen Nederlandse “g”: gram /ˈɡɹæm/, algorithm /ˈælɡəɹɪðm̩/, tag /tæɡ/, photograph /ˈfoʊ.təˌɡɹæf/, diagnostic /ˌdaɪ.əɡˈnɑ.stɪk/
Zo oefen je met deze video
- Luister de hele video één keer zonder te spreken en noteer de woorden die je niet kent.
- Begin op 0,75× snelheid, spreek zin voor zin na en ga terug naar normale snelheid zodra het makkelijk gaat.
- Neem jezelf op en vergelijk met het origineel; let daarbij op woorden als coin, label, predict.
Wat is de Shadowing-techniek?
Shadowing is een wetenschappelijk onderbouwde taalleermethode die oorspronkelijk is ontwikkeld voor professionele tolkentraining en gepopulariseerd door polyglot Dr. Alexander Arguelles. De methode is eenvoudig maar krachtig: je luistert naar native Engelse audio en herhaalt het onmiddellijk hardop — als een schaduw die de spreker volgt met slechts 1–2 seconden vertraging. In tegenstelling tot passief luisteren of grammaticadrills, dwingt shadowing je hersenen en mondspieren om echte spraakpatronen tegelijkertijd te verwerken en te reproduceren. Onderzoek toont aan dat het de uitspraaknauwkeurigheid, intonatie, ritme, verbonden spraak, luisterbegrip en spreekvaardigheid aanzienlijk verbetert — waardoor het een van de meest effectieve methoden is voor IELTS Speaking-voorbereiding en echte Engelse communicatie.