we know humans learn
from their past instructions
are given by humans but what if humans
can Turing the machines to learn from
the past data and to what humans can
do act 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 that Paul decides
this on the basis of the tempo of the song joiner
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 soling
we see that Paul likes the song with
a fast tempo and soaring intensity while
he dislikes a song with relaxed tempo
and light intensity so now we know Paul's
choices let's see Paul listens to a
new song let's name it a song a song a has
fast tempo and a soaring intensity so
it lies somewhere here looking at the data
can you guess where the ball will like
the song or not correct so Paullikes
the 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 Pete so song B lies somewhere
here with medium tempo and medium
intensity neither relaxed nor fast neither light nor soaring now can you guess where Paul likes it or not not able to guess with this Paul will
like it or dislike
it another choice is unclear correct
we
could easily classify song a but when the
choice became complicated as in the case
of song P yes and that's where machine
learning comes in let's see how in
the same example for song P if we draw
a circle around the song B we see that
there are four words for like whereas
one would for dislike if we go for
the majority words 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 P 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 project
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 1 million coins
of three different currencies say one
to be one euro and one there huh each
coin has different weights, for example, a
coin of one rupee weighs three grams
one euro weighs
seven grams and one their own 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 slip for example
it will learn that if a coin is of
three grams it will be a one rupee coin
let's give you going to the machine
on the basis of the weight of the
new coin your model will predict the currency
hence supervised learning uses labels
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 l
et's
move to
unsupervised learning and see the difference
suppose you have cricket data set
of various players with their respective
scores and thickets taken when
you feed this data set to the machine
the machine identifies the pattern
of player performance so it plops
this data with the respective Achatz
on the x axis while runs on the y axis
while looking at the data you will clearly
see that there are two clusters the
one cluster are the players who scored
high runs and took fewer wickets while
the other cluster is of the players
who scored fewer 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 Boulos
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's
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 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 l
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