Shadowing Practice: Machine Learning | What Is Machine Learning? | Introduction To Machine Learning | 2026 | Simplilearn - Learn English Speaking with Video
Creating lesson...
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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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Vocabulary and speaking notes for this lesson
This speaking lesson is built on the video “Machine Learning”. The speaker keeps coming back to these words: learning, machine, song, model, paul. This video has 122 sentences and 1351 words to shadow. The speech runs for 7:46. The speaker talks at a natural 174 words per minute, close to everyday conversation. Only 80% of the words are among the 3,000 most common in English, so the vocabulary is demanding.
Key vocabulary in this video
15 words from the video worth learning, with pronunciation and meaning:
| Word | Pronunciation | Meaning |
|---|---|---|
| coin noun | /ˈkoɪ̯n/ | A piece of currency, usually metallic and in the shape of a disc, but sometimes polygonal, or with a hole in the middle. |
| label noun | /ˈleɪ.bəl/ | A small ticket or sign giving information about something to which it is attached or intended to be attached. |
| intensity noun | /ɪnˈtɛnsɪti/ | The quality of being intense. |
| predict verb | /pɹɪˈdɪkt/ | To make a prediction: to forecast, foretell, or estimate a future event on the basis of knowledge and reasoning; to prophesy a future event on the basis of… |
| tempo noun | /ˈtɛm.poʊ/ | A frequency or rate. |
| currency noun | /ˈkʌɹ.ən.si/ | Money or other items used to facilitate transactions. |
| feedback noun | /ˈfiːdˌbæk/ | Critical assessment of a process or activity or of their results. |
| scenario noun | /sɪˈnɛəɹioʊ/ | An outline or model of an expected or supposed sequence of events. |
| axis noun | /ˈæksɪs/ | An imaginary line around which an object spins (an axis of rotation) or is symmetrically arranged (an axis of symmetry). |
| cluster noun | /ˈklʌstɚ/ | A bunch or group of several discrete items that are close to each other. |
| dislike verb | /dɪsˈlaɪk/ | To have a feeling of aversion or antipathy towards; not to like. |
| gram noun | /ˈɡɹæm/ | A unit of mass equal to one-thousandth of a kilogram. Symbol: g. |
| wicket noun | /ˈwɪkɪt/ | A small door or gate, especially one beside a larger one. |
| relax verb | /ɹɪˈlæks/ | To make something loose. |
| transaction noun | /tɹænˈzækʃən/ | The act of conducting or carrying out (business, negotiations, plans). |
Phrasal verbs you will hear
| Word | Meaning |
|---|---|
| come across verb | To change sides; to cross over to work for the opposition. |
Grammar in this video
The structures the speaker uses most, with the exact words from the video:
| Structure | In the video |
|---|---|
| Passive voice be + past participle — the focus is on what happens, not who does it | is associated · is unsupervised · was labeled |
| Present perfect have/has + past participle — a past action that still matters now | you've understood supervised · has been updated |
| Relative clauses who / which + clause — extra information about a person or thing | learning which is · those who need |
Pronunciation to watch
The speaker uses 4 contractions and reduced forms, such as don't, I'll, I'm. Say them the short way, as you hear them.
- The “sh” and “zh” sounds: transaction /tɹænˈzækʃən/, associate /əˈsoʊʃi.ət/, rush /ɹʌʃ/, genre /ˈ(d)ʒɑnɹə/, instruction /ɪnˈstɹʌkʃən/
- Long words — get the stress right: intensity /ɪnˈtɛnsɪti/, complicated /ˈkɑm.plɪˌkeɪ.tɪd/, associate /əˈsoʊʃi.ət/, accuracy /ˈæk.jɚ.ə.si/, capability /ˌkeɪ.pəˈbɪl.ɪ.ti/
How to practise with this video
- Listen to the whole video once without speaking and note the words you do not know.
- Start at 0.75× speed, shadow it sentence by sentence, then go back to normal speed once it feels easy.
- Record yourself and compare with the original, paying attention to words like coin, label, intensity.
What is the Shadowing Technique?
Shadowing is a science-backed language learning technique originally developed for professional interpreter training and popularized by polyglot Dr. Alexander Arguelles. The method is simple but powerful: you listen to native English audio and immediately repeat it out loud — like a shadow following the speaker with just a 1–2 second delay. Unlike passive listening or grammar drills, shadowing forces your brain and mouth muscles to simultaneously process and reproduce real speech patterns. Research shows it significantly improves pronunciation accuracy, intonation, rhythm, connected speech, listening comprehension, and speaking fluency — making it one of the most effective methods for IELTS Speaking preparation and real-world English communication.