we 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
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 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
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
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