Okay, let's get down to business.
Using the simple diagram representing an ML
system, we have input, output, and some
brain with knowledge in the middle.
In machine learning, features are the input data.
A single feature represent a specific input variable.
Think about the task of predicting
the market price of a house.
A single feature can be the size of
that house, but this is not enough.
We will need more relevant features that
can better represent a house, so the
system can better predict the market price.
The features that can be used as input to
such ML system can be the size, number of
bedroom, street area, overall condition, and so on.
Another example will be an email spam detector.
And in that case, the features of such ML system
can be the content of the email like the sender
address, the time and the hour of the day
the email was generated and sent, number of spammy words
like free, money, and so on.
In simple projects we can have 10-20 input
features like we just saw, while in some
very sophisticated machine learning project could use thousands
and even millions of features.
Mathematically, the list of input features will be represented
as a vector with the size of n like
x1, x2, x3, until xn.
As the sentence is saying garbage
in, we will get garbage out.
We'll talk about it later in this training,
but it is important to mention that selecting the
right features is a critical step in the process.
Something that is called feature selection.
Okay, moving next, a label is the
output of the machine learning system.
It is the thing we would like
to predict or classify using the system.
After we train that system, a label could
be the price of a real estate property.
As we just talked about, the identified object in a picture
like this is a dog, that's a cat, and so on.
The type of incoming email, spam, not spam, the
root cause of some events, the words being used
in a video clip and much more.
Mathematically, the label will be presented as Y as a
simple math equation Y equal to the function of x.
Now we can describe the meaning of
examples, which is another key term.
An example is a single instance of
data represented by the letter x.
There are two types of examples
labeled examples and unlabeled examples.
A labeled examples includes the features, which
is the x and also the label
related to such features which is Y.
Think about an email, a user
manually labeled as a spam.
It is a labeled examples.
Or think about an image as input,
which is basically many small pixels.
All those pixels are input features and
inside the picture there is a specific
object, like a specific type of animal.
The label will be the name of that animal, okay,
the type of that animal.
If we have the image file and the type
of animal inside, then we have one labeled example.
If we have just the image file without the
identified object inside, then we have an unlabeled example.
A large group of labeled examples can be used
as a training data set to train a model.
If I will summarize we have features as input to
the machine learning system, a label as the expected output.
If we have a group of data instances that are
already labeled, then they can be used as examples to
train model, which is called a training data et.
Moving next, let's talk about training a model.
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