We know humans learn from their past experiences
and machines follow instructions given by humans but what if humans can turn
the machines to learn from the past data and to 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, for example, Paul loves listening to new songs he either
likes them or dislikes. Paul decides this on the basis of the song's tempo
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 soar. We see that Paul likes the song with a fast tempo and soaring intensity while
he dislikes a song with a 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 has a fast tempo and a soaring intensity. So it lies somewhere with previous
one looking at the data can you guess where Paul 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. Label
it as song B, so song B lies somewhere between medium tempo and medium
intensity, neither relaxed nor fast, neither light nor soaring. Now can you guess
where Paul likes it or not notable 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 B 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 words for like whereas one
would for dislike, if we go for the majority of 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.
Therefore, 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 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
euro and 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. Our 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 a coin 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 the labels associated with those features on
this note. Let's move to unsupervised learning and see the difference. Suppose
you have a cricket dataset of various players with respective scores and wickets were taken. When you feed this data-set to the machine, the machine identifies the pattern
of player performance. It plots this data with the respective wickets on the x-axis
while scores 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 bowlers. 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 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 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 the machine learning model.
Let's have a quick quiz, you have to determine
whether the given scenarios use supervised or unsupervised learning. So now
you want 1) Facebook recognizes your friend in a picture from an album of tagged
photographs 2) Netflix recommends new movies based on someone's past movie
choices 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 the analysis. In
addition, the memory handling capabilities of computers have largely increased
which helps them to process such a huge amount of data at hand without any
delay and yes, computers now have great computational powers.
There are a lot of applications of machine
learning out thereto name a few.
Machine learning is used in healthcare where
Diagnostics are predicted for doctors' reviews. 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 the cab you must have encountered
surge pricing often where it says that the field trip has been updated continue
booking. 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 are doing amazing jobs so
that's all for machine learning basics today from my side keep reading the
space for more interesting articles until then happy learning.
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