Welcome to Questpond's youtube channel. Today we will be learning
some basic fundamentals of Machine Learning.
Before we start with things will run through some very important, cute and heart touching comments
which people have put on our channel.
Specifically we will be starting with Himal sir
Thank you so much Himal sir because of subscribers like you we are able to make such videos.
We exist because of readers like you, you have supported us for years together.
As a return gift
we are providing you a lifetime subscription. Yes you hear it right.
free so that you are associated with us till whatever time we can teach.
The next comment is coming from Priya Bist
This video should address this comment. In this video we are trying to
understand the basics of Machine Learning
Will start with Machine Learning, introduction of Machine learning
will talk about algorithm and training data
will also talk about vectors in Machine Learning, what exactly is a model in Machine Learning
Will talk about features and labels which define the input and output of machine learning
then will talk about BOW i.e Bag of Words.
Let us start with what is
Machine Learning?
Machine Learning
is an area of study
where we want
machines
to think like humans
and act like humans.
This journey of
going from a machine
to a human
or making a machine
think like human it atleast needs two things
One is it needs some kind of Thinking
humans can think.
Second
humans get experience get mature, they learn from surroundings
learn from school
learn from the parents
and so on.
Somehow in the machine we need to atleast put
two things that are to make the machine to think
and second one is
we need to see the machine gets matured by learning.
The thinking power in a machine can be brought in by
some kind of Algorithm.
We
need to create the thinking power of machine by putting some algorithm. Depending on situations we can have different kinds of Algorithm
The first thing what machine learning
needs is Algorithm.
Then this Algorithm
will provide training data
and it will train them.
The experience part is brought in by providing some kind of training data to the algorithm.
One is we have algorithm thinking
and the experience or the maturing power we can do it by providing it training data.
Computers only understand numbers
so when we provide it
numbers it get trained very easily.
But if we have text, audio, video and images
we need to convert those
kind of formats
and things into
numbers. Those numbers are termed as
Vector
If we have the Machine Learning data
pure numbers is very easy.
But if we have some kind of text
audio, image or some kind of document
then we need to convert that document into numbers and this is termed as vector.
Vector is a collection of numbers.
We have an Algorithm
we are training that Algorithm
and by doing so
we are having an experience algorithm.
This experience algorithm is termed as a Model.
A Model is an algorithm
with some kind of experience it has got from training data.
The Model is the most important part of
any Machine Learning project .
To start with a Machine Learning project the first thing
should come to our mind is
what kind of Model we are expecting at the end of the day.
Model means
trained algorithm.
When we training a machine by using some kind of training data
we need to provide that training data a format of
features and labels .
These features and labels are the input and output
Features are input
and Labels are output.
What are features?
Features are the important characteristic
which is extracted from a text and describe an object, thing, entity or anything we want the machines to learn.
We can say Red, Round and Sweet
will tell the machine when you see this Red, Round and Sweet
think it is an Apple.
When you see a Cone Shape,
Yellow and Grained
think it is a Maize.
When you see Yellow, Sweet and Juicy
think it is a Mango.
Features are inputs and labels are things which we want machine to think about that features.
Whenever we are providing training data we need to provide in terms of Features and Labels.
As we discussed Features are a very very important part of Machine Learning.
We need to have mechanism by which we can
extract features from the training data.
Somebody provides training data in text format,
audio format or in video format
we need to somehow extract features from that text
and then later on
we need to label features and that will become as a training input to the Machine.
There are lots of time tested mechanism which are available
to extract the features.
The Simplest mechanism which is available is
BOW - Bag of Words.
Bag of Words
is a concept
or a process by which we can extract features from a free text.
Bag of Words is an simplifying representation
of words for a big textual document.
We have a textual two line sentence which says
Today we will learn how to cook vegetables.
To cook vegetables we need to first wash them.
For this we can extract important words which can represent these two sentences.
We have extracted Today,
Learn, Cook
Vegetables and Wash
Bag of Words does not really check grammars
or try to check where the position of the word is
it just try to extract important words.
One of the
process is that
it tries to avoid stop words.
We can have lot of those verbs like and, the
which probably does not give us simplifying representation of the document.
We can apply stop words
like we are not extracting we, will, how, to, them.
We can apply the stop words crosses by this we can only extract important words from that two liner text.
One of the parts of Bag of Words
which we do is
we also try to get the no. of occurrence.
In this document cook has occurred two times
so we have also noted down the no. of occurrence.
Later on
these important bag of words we can convert them into numbers i.e Vectors
and feed it to the machine
then label these features
so that machine can get trained.
Bag of Words is a simplifying representation of a text
with a no. of occurrences
but it does not take into consideration the grammar or where the words come first or it comes last.
That brings us to the end of the video, in this video we were trying to understand important fundamentals like
basics of Machine Learning, Algorithms, training data, vectors, models, features, labels, BOW and so on.
As a next step in Machine Learning we would encourage you to watch
the Python videos.
On the screen
we have flashed
the Python 1 hour of tutorial
If you go through this tutorial you should be very much comfortable with Python.
Thank you very much! Happy Learning