Hello, everyone.
Now, we have gathered all of the important skills which are required to begin our joining the data
science.
We know what fighting is, how to write the code and him, we are well aware of statistics, so now
is the point.
Then we will begin with the actual machine gun.
And you must know that a major part of machine learning lifecycle goes into the.
We have a lot of data in hand.
We have data from the booths, from different websites or feedback forums, and these data are present
in a structured form and also an unstructured form.
Structured form could be in Excel files or devious and unstructured forms, could be meby or tweet.
So all of this data needs to be collected, clean and some specific data needs to be selected, which
could actually be used for data.
So in this particular module, we will discuss about data preparation, which will include that.
What are the different types of features available?
And then once we identified different types of features, then we will check if there is any type of
missing noisey or outlier data prison.
And we will handle this type of data once we handle this data.
Now, whatever we have is a complete data, but some of these columns might be useful and some may not
be.
And sometimes you might want to create new problems so that we can retrieve important information from
that.
So for that particular task, we will be creating new columns using the existing columns in hand, we
will be selecting some columns out of all the features which we have, and we will be removing some
columns which are not really required.
So all of these things we will be doing as a part of the preparation and finally.
We will convert everything into a new medical now different type of data for the structured data on
actual data.
So as part of data preparation, we will learn how to handle the new categorical unstructured data,
the data which we get from a tablet form.
And we will also learn how we can work on fixed data.
So these are two things which we will be learning in the Depression and we will see how we can work
using two very important Lively's that is number by and find us.
And we will also learn how we can analyze the data using visualization libraries like from my lab and
tiedemann.
And for tax data, we will see how we can use libraries like Analytica so that we can retrieve important
attributes, important features from the FDIC's data.
So I hope you will learn a lot from this particular session.
Thank you.