Well, hello, dear Friends, says you may remember in our previous films, we've talked about learning
methods, but it was not so deep.
We just mentioned it so in this class and we're going to try to get into the supervised machine learning
methods and we're going to go deep.
All right.
So as you may remember, we said that we give data and answers at the same time to a computer, the
computer can then learn the rules and in the future, it can generate original answers.
So this approach is called machine learning.
The process is named learning.
So machine learning was indeed created to solve problems in situations where analytical models are inadequate.
When equations and laws aren't very promising, well, we use machine learning techniques to derive
a model using training data.
So I'll show you here, we need data for machine learning if we got enough data and answers and the
A.I. can create the rules.
Today on the planet Earth, we've got some very big data.
In fact, it increases every day, I won't even try to calculate by how much.
But we do try to figure out how to use it.
So this is a problem, and for this problem, developers thought that, well, why don't we try to apply
machine learning and they've achieved something.
And every day they will achieve something new.
So basically, we're going to try to develop the machine learning algorithms to create programs that
can learn from data and develop with experiences without any intervention.
So our algorithm models will be able to categorize three different categories supervised learning,
unsupervised learning, reinforcement learning.
So our first category is supervised learning, and in this lesson and in our next lesson, we're going
to examine this category a little deeper.
So let's get started.
So supervised learning is an approach to creating artificial intelligence where the program is given
labeled input data and the expected output results.
Yeah, I system is specifically told what to look for, thus the model is train until it can detect
the underlying patterns and relationships by itself, enabling it to yield good results when presented
with never before seen data.
Supervised learning is good at classification and regression problems, such as determining what category
a news article belongs to or predicting the volume of sales for a given future date.
In the supervised learning, the aim is to make sense of data with the specific measurements in mind.
In contrast to supervised learning is the unsupervised learning method, which tries to make sense of
the data in itself.
Does that make sense?
So in other words, there are no external measurements or guidelines and unsupervised learning, the
algorithm just has to comprehend the data and detect the patterns or similarities.
OK, great, but how does a supervised learning model work?
Like all machine learning algorithms, supervised learning is based on training, the system is fed
with massive amounts of data during its training phase, which instructs his system what should be output
and what should be obtained from each specific input value.
The train model is then presented with test data to verify the result of the training and measure the
accuracy.
And neural network algorithms is supervised, learning process is improved by constantly measuring the
resulting output of the model and fine tuning the system to get closer to its target accuracy.
A level of accuracy obtainable will depend on two things, the data available and the algorithm in use.
High accuracy is not necessarily a really good indication, by the way.
It could also mean that the model is suffering from overfitting, and what I mean by that is it is over
tuned to its particular training dataset.
So such a data set might perform well in test scenarios, but failed miserably when presented with real
world challenges.
So to avoid Overfitting, it's important that the test data is different from the training data to ensure
that the model is not drawing answers from its previous experience, but instead the models inference
is generalised.
So training data must also be balanced and cleaned.
Garbage or duplicate data will skew the eyes, understanding and data scientists must be careful with
the data.
The model is trained on.
Also, the diversity of the data determines how well I will perform when presented with new cases.
If there's not enough samples in the training data set, the model will falter and will fail to yield
any reliable answers.
So the algorithm, on the other hand, determines how that data can be put to use, for instance, deep
learning algorithms can be trained to extract billions of parameters from their data and reach unprecedented
levels of accuracy as demonstrated by Open Eyes three.
Apart from neural networks, there are many other supervised learning algorithms, including support
vector machines, EVMs, linear regression, logistic regression, naive bais and decision trees.
Supervised learning algorithms primarily generate two kinds of results, classification and regression.
So classification algorithm.
That's going to try to determine the class or the category of the data that is presented with.
For instance, object recognition algorithms or classification problems where the A.I. is tasked to
determine what category of objects the item it is presented with belongs to.
Character recognition, email, spam classification, sentiment analysis and drug classification, these
are all examples of problems requiring the A.I. to determine what class the data belongs to.
How many times an object might belong to several categories and the FBI will need to determine what
those categories are and how much confidence the algorithm has in his predictions.
Now, regression tasks are different.
They expect the model to produce a numerical value, for instance, predicting click rates and online
ads, predicting real estate prices or determining how much a customer would be willing to pay for a
certain product.
So common supervised machine learning algorithms include the following.
Linear regression.
Logistic regression.
Artificial neural networks or and's.
Decision trees.
Bayesian logic as VMS Random Forest.
So when you're choosing a supervised learning algorithm, there are a few things that should be considered.
The first is the bias and variance that exist within the algorithm.
As there's a fine line between being flexible enough and being too flexible.
Another is the complexity of the model or function of the system is trying to learn.
Additionally, the heterogeneity, accuracy, redundancy and linearity of the data should also be analyzed
before choosing an algorithm.
All right, so that's it for now in this lesson, we talked about the supervised learning method.
So in the next lesson we're going to examine them one by one.
So I want to see in the next lesson.
All right.
Till then, have a nice day.