Welcome to whiteboard programming, where we
simplify programming with easy to understand
whiteboard videos.
And today, we'll be understanding the difference
between Artificial intelligence, Machine Learning,
Deep Learning, and Data Science
These all are big buzzwords, right?
But knowing when to use which one is quite
important... so lets break it down
1.
Data Science
Data science is all about data, and I’m
pretty sure you already knew that.
We all know that every single tech company
out there is collecting huge amounts of data from us.
And data in the 21st century is revenue.
Why is that?
That’s because of data science.
The more data you have, the more business
insights you can generate.
Using data science, you can uncover patterns
in data that you didn’t even knew existed.
For example, you can discover that some guy
who went to New York City for a vacation is
most likely to splurge on a luxury trip to
Venice in the next three weeks.
And if you’re a company offering luxury
tours to exotic destinations, you might be
interested in getting this guy’s contact
number.
Just like this, Companies are using data science
to build recommendation engines, predicting
user behaviour, and doing so much more.
And all of this is only possible when you
have enough amount of data to get accurate
results that can be applied to a business
use case.
Next, There is also something called as prescriptive
analytics in data science, which does pretty
much the same predictions that we talked about
in the rich tourist example above.
But as an added benefit, prescriptive analytics
will also tell you what kind of luxury tours
to Venice a person might be interested in.
For example, one person might want to fly
first class but would be fine with a three
star accommodation, whereas another person
could be ready to fly economy but definitely
needs the most luxurious stay and cultural
experience of Venice.
So even though both these people will be your
rich clients, both of them have different
requirements.
So, in this scenario, prescriptive analytics will help you a lot
You might be wondering, hey, that sounds a
lot like artificial intelligence.
And you’re not entirely wrong, actually.
Let’s see how.
2.
Artificial Intelligence
Artificial intelligence, or AI for short,
is the ability that can be imparted to computers
which enables these machines to understand
data, learn from the data, and make decisions
based on patterns hidden in the data, or inferences
that could otherwise be very difficult (or
almost impossible) for humans to make manually.
AI also enables machines to adjust their “knowledge”
based on new inputs that were not part of
the data used for training these machines.
But there’s one thing you need to make sure,
that you have enough data for AI to learn
from.
If you have a very small data lake that you’re
using to train your AI model, the accuracy
of the prediction or decision could be low.
So more the data, better is the training of
the AI model, and more accurate will be the
outcome.
Depending on the size of your training data,
you can choose various algorithms for your
model.
And this is where machine learning and deep learning
start to show up.
3.
Machine Learning
Machine Learning (ML) is considered a sub-set
of AI.
You can even say that ML is an implementation
of AI.
So whenever you think of AI, you can think
of applying ML there.
As the name makes it pretty clear, ML is used
in situations where we want the machine to
learn from the huge amounts of data we give
it, and then apply that knowledge on new pieces
of data that is streamed into the system.
But you might be thinking, how does a machine
learn?
Well, There are different ways of making a
machine learn, these broadly include supervised
learning, non-supervised learning, semi-supervised
learning, and reinforced machine learning.
In some of these methods, a user tells the
machine what are the features or independent
variables (input) and which is the dependent
variable (output).
So the machine learns the relationship between
the independent and dependent variables present
in the data that is provided to the machine.
This data which is provided is called the
training set.
And once the learning phase or the training
is complete, the machine, or the ML model,
is tested on a piece of data which the model
has not encountered before.
This new dataset is called the test dataset.
Now on comparing actual results with the one
you got from the model, you gain how accurately
the machine has learned.
There are many types of models and algorithms
that can be employed to do this and I have
created a short 5 minute video to help you
get familiar with the basics, be sure to check
the video link in the description.
Moving forward, do note that there are a lot
of data preparation or pre-processing steps that
you need to take care of even before training
your model.
But ML libraries such as SciKit Learn have
evolved so much that even an app developer
without any background in mathematics or statistics,
or even a formal AI education, can start using
these libraries to build, train, test, deploy,
and use ML models in the real world.
But it always helps to know how these algorithms
work, as they will help you to make informed decisions
when you are to select an algorithm for your
problem statement.
With this knowledge of ML, let’s talk a
bit about deep learning now.
4.
Deep Learning
Deep Learning (DL) is an advancement of ML,
and is regarded as a subset of Machine learning.
And while ML is super powerful for most applications,
there are situations where ML leaves a lot
to be desired.
That is where deep learning steps in.
It is generally believed that if your training
dataset is relatively small, you go with ML.
But if you have huge amounts of data on which
you can train a model, and if the data has
too many features, along with accuracy being super
important in your case, you take the deep
learning route.
It is also important to note that deep learning
requires much powerful hardware to run, that's
why, mostly GPUs are used, and it takes significantly
more time to train your models, and it is
generally more difficult to implement compared
to ML.
But these are some of the compromises that
you have to live with when the problem you’re
trying to solve is that much more complex.
Also, You might have heard of TensorFlow,
which is a neural network that Google is extensively
using and pushing to developers.
It is the future advancements of deep learning
that today we have the vision of building
self driving cars, or are even able to use
complex tools like google translate that are
able to translate big paragraphs of text from
one language to another in a matter of milliseconds.
With that, I hope this video was helpful to
you and served value.
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