MACHINE LEARNING:

 we know humans learn from their pastwe 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 the 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 relaxed 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 would 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 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

supervised learning

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 one

million coins of three different

currencies say one rupee one euro and

one dirham each coin has different

weights for example a coin of one rupee

weighs three grams one euro weighs seven

grams and one dirham weighs four grams

your model will predict the currency of

the coin here your weight becomes the

feature of coins while currency becomes

the 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

unsupervised learning and see the

difference suppose you have cricket data

set of various players with their

respective scores and wickets taken when

you 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'll clearly see

that there are two clusters the one

cluster are the players who scored

higher 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

reinforcement learning

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

reinforcement 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 one 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 is

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 where

diagnostics are predicted for doctor's

review the sentiment analysis that the

tech giants are doing on social media is

another interesting application of

machine learning fraud detection in the

finance sector and also to predict

customer churn in the e-commerce sector

while booking a gap you must have

encountered surge pricing often where it

says the fair of your trip has been

updated continue booking yes please i'm

getting late for office

Uber example

well that's an interesting machine

learning model which is used by global

taxi giant uber and others where they

have differential pricing in real time

based on demand the number of cars

available bad weather rush r etc so they

use the surge pricing model to ensure

that those who need a cab can get one

also it uses predictive modeling to

predict where the demand will be high

with the goal that drivers can take care

of the demand and search pricing can be

minimized great hey siri can you remind

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

remind you

thanks no problem comment below some

interesting everyday examples around you

where machines are learning and doing

amazing jobs so that's all for machine

learning basics today from my site keep

watching this space for more interesting

videos until then happy learningwe 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 the 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 relaxed 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 would 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 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

supervised learning

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 one

million coins of three different

currencies say one rupee one euro and

one dirham each coin has different

weights for example a coin of one rupee

weighs three grams one euro weighs seven

grams and one dirham weighs four grams

your model will predict the currency of

the coin here your weight becomes the

feature of coins while currency becomes

the 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

unsupervised learning and see the

difference suppose you have cricket data

set of various players with their

respective scores and wickets taken when

you 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'll clearly see

that there are two clusters the one

cluster are the players who scored

higher 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

reinforcement learning

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

reinforcement 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 one 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 is

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 where

diagnostics are predicted for doctor's

review the sentiment analysis that the

tech giants are doing on social media is

another interesting application of

machine learning fraud detection in the

finance sector and also to predict

customer churn in the e-commerce sector

while booking a gap you must have

encountered surge pricing often where it

says the fair of your trip has been

updated continue booking yes please i'm

getting late for office

Uber example

well that's an interesting machine

learning model which is used by global

taxi giant uber and others where they

have differential pricing in real time

based on demand the number of cars

available bad weather rush r etc so they

use the surge pricing model to ensure

that those who need a cab can get one

also it uses predictive modeling to

predict where the demand will be high

with the goal that drivers can take care

of the demand and search pricing can be

minimized great hey siri can you remind

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

remind you

thanks no problem comment below some

interesting everyday examples around you

where machines are learning and doing

amazing jobs so that's all for machine

learning basics today from my site keep

watching this space for more interesting

videos until then happy learning

and machines follow instructions given

by humans

but what if humans can train the

machines to learn from the 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

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 relaxed 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 would 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 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

supervised learning

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 one

million coins of three different

currencies say one rupee one euro and

one dirham each coin has different

weights for example a coin of one rupee

weighs three grams one euro weighs seven

grams and one dirham weighs four grams

your model will predict the currency of

the coin here your weight becomes the

feature of coins while currency becomes

the 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

unsupervised learning and see the

difference suppose you have cricket data

set of various players with their

respective scores and wickets taken when

you 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'll clearly see

that there are two clusters the one

cluster are the players who scored

higher 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

reinforcement learning

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

reinforcement 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 one 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 is

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 where

diagnostics are predicted for doctor's

review the sentiment analysis that the

tech giants are doing on social media is

another interesting application of

machine learning fraud detection in the

finance sector and also to predict

customer churn in the e-commerce sector

while booking a gap you must have

encountered surge pricing often where it

says the fair of your trip has been

updated continue booking yes please i'm

getting late for office

Uber example

well that's an interesting machine

learning model which is used by global

taxi giant uber and others where they

have differential pricing in real time

based on demand the number of cars

available bad weather rush r etc so they

use the surge pricing model to ensure

that those who need a cab can get one

also it uses predictive modeling to

predict where the demand will be high

with the goal that drivers can take care

of the demand and search pricing can be

minimized great hey siri can you remind

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

remind you

thanks.