Machine Learning.vtt

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we know humans learn from their past

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experiences

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and machines follow instructions given

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by humans

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but what if humans can train the

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machines to learn from the past data and

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do what humans can do and much faster

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well that's called machine learning but

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it's a lot more than just learning it's

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also about understanding and reasoning

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so today we will learn about the basics

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of machine learning

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so that's paul he loves listening to new

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songs

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he either likes them or dislikes them

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paul decides this on the basis of the

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song's tempo

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genre

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intensity and the gender of voice for

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simplicity let's just use tempo and

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intensity for now so here tempo is on

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the x axis ranging from relaxed to fast

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whereas intensity is on the y axis

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ranging from light to soaring we see

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that paul likes the song with fast tempo

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and soaring intensity while he dislikes

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the song with relaxed tempo and light

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intensity so now we know paul's choices

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let's say paul listens to a new song

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let's name it as song a song a has fast

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tempo and a soaring intensity so it lies

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somewhere here looking at the data can

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you guess whether paul will like the

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song or not correct so paul likes this

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song by looking at paul's past choices

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we were able to classify the unknown

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song very easily right let's say now

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paul listens to a new song let's label

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it as song b so song b

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lies somewhere here with medium tempo

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and medium intensity neither relaxed nor

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fast neither light nor soaring now can

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you guess whether paul likes it or not

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not able to guess whether paul will like

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it or dislike it are the choices unclear

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correct we could easily classify song a

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but when the choice became complicated

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as in the case of song b yes and that's

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where machine learning comes in let's

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see how in the same example for song b

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if we draw a circle around the song b we

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see that there are four votes for like

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whereas one would for dislike if we go

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for the majority votes we can say that

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paul will definitely like the song

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that's all this was a basic machine

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learning algorithm also it's called k

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nearest neighbors so this is just a

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small example in one of the many machine

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learning algorithms quite easy right

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believe me it is but what happens when

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the choices become complicated as in the

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case of song b that's when machine

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learning comes in it learns the data

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builds the prediction model and when the

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new data point comes in it can easily

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predict for it more the data better the

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model higher will be the accuracy there

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are many ways in which the machine

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learns it could be either supervised

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learning unsupervised learning or

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reinforcement learning let's first

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quickly understand supervised learning

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suppose your friend gives you one

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million coins of three different

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currencies say one rupee one euro and

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one dirham each coin has different

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weights for example a coin of one rupee

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weighs three grams one euro weighs seven

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grams and one dirham weighs four grams

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your model will predict the currency of

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the coin here your weight becomes the

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feature of coins while currency becomes

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the label when you feed this data to the

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machine learning model it learns which

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feature is associated with which label

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for example it will learn that if a coin

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is of 3 grams it will be a 1 rupee coin

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let's give a new coin to the machine on

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the basis of the weight of the new coin

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your model will predict the currency

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hence supervised learning uses labeled

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data to train the model here the machine

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knew the features of the object and also

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the labels associated with those

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features on this note let's move to

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unsupervised learning and see the

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difference suppose you have cricket data

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set of various players with their

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respective scores and wickets taken when

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you feed this data set to the machine

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the machine identifies the pattern of

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player performance so it plots this data

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with the respective wickets on the

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x-axis while runs on the y-axis while

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looking at the data you'll clearly see

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that there are two clusters the one

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cluster are the players who scored

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higher runs and took less wickets while

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the other cluster is of the players who

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scored less runs but took many wickets

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so here we interpret these two clusters

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as batsmen and bowlers the important

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point to note here is that there were no

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labels of batsmen and bowlers hence the

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learning with unlabeled data is

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unsupervised learning so we saw

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supervised learning where the data was

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labeled and the unsupervised learning

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where the data was unlabeled and then

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there is reinforcement learning which is

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a reward based learning or we can say

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that it works on the principle of

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feedback here let's say you provide the

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system with an image of a dog and ask it

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to identify it the system identifies it

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as a cat so you give a negative feedback

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to the machine saying that it's a dog's

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image the machine will learn from the

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feedback and finally if it comes across

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any other image of a dog it will be able

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to classify it correctly that is

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reinforcement learning to generalize

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machine learning model let's see a

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flowchart input is given to a machine

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learning model which then gives the

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output according to the algorithm

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applied if it's right we take the output

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as a final result else we provide

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feedback to the training model and ask

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it to predict until it learns i hope

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you've understood supervised and

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unsupervised learning so let's have a

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quick quiz you have to determine whether

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the given scenarios uses supervised or

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unsupervised learning simple right

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scenario one facebook recognizes your

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friend in a picture from an album of

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tagged photographs

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scenario 2 netflix recommends new movies

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based on someone's past movie choices

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scenario 3 analyzing bank data for

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suspicious transactions and flagging the

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fraud transactions think wisely and

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comment below your answers moving on

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don't you sometimes wonder how is

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machine learning possible in today's era

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well that's because today we have

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humongous data available everybody is

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online either making a transaction or

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just surfing the internet and that's

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generating a huge amount of data every

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minute and that data my friend is the

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key to analysis also the memory handling

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capabilities of computers have largely

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increased which helps them to process

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such huge amount of data at hand without

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any delay and yes computers now have

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great computational powers so there are

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a lot of applications of machine

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learning out there to name a few machine

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learning is used in healthcare where

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diagnostics are predicted for doctor's

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review the sentiment analysis that the

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tech giants are doing on social media is

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another interesting application of

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machine learning fraud detection in the

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finance sector and also to predict

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customer churn in the e-commerce sector

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while booking a gap you must have

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encountered surge pricing often where it

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says the fair of your trip has been

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updated continue booking yes please i'm

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getting late for office

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well that's an interesting machine

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learning model which is used by global

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taxi giant uber and others where they

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have differential pricing in real time

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based on demand the number of cars

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available bad weather rush r etc so they

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use the surge pricing model to ensure

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that those who need a cab can get one

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also it uses predictive modeling to

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predict where the demand will be high

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with the goal that drivers can take care

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of the demand and search pricing can be

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minimized great hey siri can you remind

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me to book a cab at 6 pm today ok i'll

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remind you

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thanks no problem comment below some

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interesting everyday examples around you

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where machines are learning and doing

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amazing jobs so that's all for machine

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learning basics today from my site keep

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watching this space for more interesting

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videos until then happy learning