What is Machine Learning and How Its Works
We
know humans learn from their past experiences and machines follow instructions
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 songs.
tempo johner intensity and the gender of voice for simplicity see that Paul likes the song with 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 Paul likes 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 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 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 if a coin is of three grams it
will be a one rupee coin the
What is Machine Learning and How Its Works
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 let's move to
unsupervised learning and scores and thickets taken this data cluster are the
players who scored high 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 batsman 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 a 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
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 learning to generalize machine learning model let's see a flowchart
input is given if it's right we take the output as a final result else we
provide feedback to the train 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 use the supervised
or unsupervised learning simple right so now you want 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
this in Aisle 3 analyzing Bank data for suspicious transactions and flagging
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 a
What is Machine Learning and How Its Works
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
doctors review the sentiment analysis that the tech giants are doing on social
media is another interesting application machine learning fraud detection in
the finance sector and also to predict customer churn in the e-commerce sector while
booking the gap you must have encountered surge pricing often where it says the
Farrow field trip has been updated continue cooking 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 hour 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 surge pricing can be
minimized great hey Siri can you remind me to book a cab at 6 p.m. today ok
I'll remind you Thanks comment below some interesting everyday examples around
you where machines are learning and doing amazing jobs so that's all for
machine learning basics happy learning.
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