Pratica di Shadowing: Machine Learning | What Is Machine Learning? | Introduction To Machine Learning | 2026 | Simplilearn - Impara a parlare inglese con i video
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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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Vocabolario e note di pronuncia per questa lezione
Questa lezione di conversazione si basa sul video “Machine Learning”. Le parole che ritornano più spesso: learning, machine, song, model, paul. Questo video contiene 122 frasi e 1351 parole da ripetere con lo shadowing. Il parlato dura 7:46. Chi parla ha un ritmo naturale di circa 174 parole al minuto, vicino a una conversazione quotidiana. Solo il 80% delle parole rientra nelle 3.000 più comuni dell’inglese, quindi il lessico è impegnativo.
Vocaboli chiave di questo video
15 parole del video che vale la pena imparare, con pronuncia e significato:
| Parola | Pronuncia | Significato |
|---|---|---|
| coin sostantivo | /ˈkoɪ̯n/ | moneta |
| label sostantivo | /ˈleɪ.bəl/ | etichetta |
| intensity sostantivo | /ɪnˈtɛnsɪti/ | intensità |
| predict verbo | /pɹɪˈdɪkt/ | profetizzare |
| tempo sostantivo | /ˈtɛm.poʊ/ | ritmo |
| currency sostantivo | /ˈkʌɹ.ən.si/ | valuta |
| feedback sostantivo | /ˈfiːdˌbæk/ | valutazione, riscontro |
| scenario sostantivo | /sɪˈnɛəɹioʊ/ | scenario, panorama |
| axis sostantivo | /ˈæksɪs/ | asse |
| cluster sostantivo | /ˈklʌstɚ/ | gruppo |
| dislike verbo | /dɪsˈlaɪk/ | non piacersi |
| gram sostantivo | /ˈɡɹæm/ | grammo |
| algorithm sostantivo | /ˈælɡəɹɪðm̩/ | algoritmo |
| weigh verbo | /weɪ/ | pesare |
| complicated aggettivo | /ˈkɑm.plɪˌkeɪ.tɪd/ | complicato, intricato |
I phrasal verb che sentirai
| Parola | Significato |
|---|---|
| come across verbo | dare l'impressione, avere l'aria |
La grammatica di questo video
Le strutture che chi parla usa di più, con le parole esatte del video:
| Struttura | Nel video |
|---|---|
| Forma passiva be + participio passato — conta ciò che accade, non chi lo fa | is associated · is unsupervised · was labeled |
| Present perfect have/has + participio passato — un’azione passata che conta ancora adesso | you've understood · has been updated |
| Frasi relative who / which + frase — un’informazione in più su una persona o una cosa | learning which is · those who need |
Pronuncia a cui fare attenzione
Chi parla usa 4 contrazioni e forme ridotte, come don't, I'll, I'm. Pronunciale nella forma breve, così come le senti.
- I suoni “sh” e “zh”: transaction /tɹænˈzækʃən/, associate /əˈsoʊʃi.ət/, rush /ɹʌʃ/, genre /ˈ(d)ʒɑnɹə/, instruction /ɪnˈstɹʌkʃən/
- Parole lunghe — attenzione all’accento: intensity /ɪnˈtɛnsɪti/, complicated /ˈkɑm.plɪˌkeɪ.tɪd/, associate /əˈsoʊʃi.ət/, accuracy /ˈæk.jɚ.ə.si/, capability /ˌkeɪ.pəˈbɪl.ɪ.ti/
I suoni difficili per chi parla italiano:
- /h/ — va soffiata, non è muta: whereas /(h)wɛɹˈæz/, hence /ˈhɛns/, humongous /ˌhjuːˈmʌŋɡəs/
- Consonante finale — senza aggiungere una vocale dopo: predict /pɹɪˈdɪkt/, feedback /ˈfiːdˌbæk/, axis /ˈæksɪs/, dislike /dɪsˈlaɪk/, wicket /ˈwɪkɪt/
- /æ/ — più aperta della “e”: feedback /ˈfiːdˌbæk/, axis /ˈæksɪs/, gram /ˈɡɹæm/, relax /ɹɪˈlæks/, transaction /tɹænˈzækʃən/
Come esercitarsi con questo video
- Ascolta tutto il video una volta senza parlare e annota le parole che non conosci.
- Inizia a velocità 0,75×, fai shadowing frase per frase e torna alla velocità normale quando diventa facile.
- Registrati e confronta con l’originale, facendo attenzione a parole come coin, label, intensity.
Cos'è la tecnica dello Shadowing?
Shadowing è una tecnica di apprendimento delle lingue supportata da studi scientifici, originariamente sviluppata per la formazione dei traduttori professionisti e resa popolare dal poliglotta Dr. Alexander Arguelles. Il metodo è semplice ma potente: ascolti un audio in inglese di madrelingua e lo ripeti immediatamente ad alta voce — come un'ombra che segue il parlante con un ritardo di solo 1–2 secondi. A differenza dell'ascolto passivo o degli esercizi di grammatica, lo shadowing costringe il tuo cervello e i muscoli della bocca a elaborare e riprodurre simultaneamente i modelli di discorso reale. La ricerca dimostra che migliora significativamente la precisione della pronuncia, l'intonazione, il ritmo, il discorso connesso, la comprensione dell'ascolto e la fluidità del parlato — rendendolo uno dei metodi più efficaci per la preparazione alla prova di speaking dell'IELTS e per la comunicazione reale in inglese.