Hi and welcome back to our last section
in this training. I would like to recap the things
we covered so far for creating an end-to-end story.
Artificial intelligence is an umbrella term for the fundamental
idea that some complex intellectual tasks that are performed
by humans can also be performed by machines.
When a machine can mimic or complex cognitive functions
like identifying an object in a picture or
recognize a human voice and many other complex
tasks, it is described as artificial intelligence.
Still machines can perform complex tasks using
pre-programmed rules, but in many cases this
traditional programming approach cannot scale up to
handle very complex tasks.
Something is missing here.
The missing part of AI is the flexibility
to learn, which is the next phase of
the AI evolution path moving to the concept
of machine learning, machines that can learn.
So instead of using pre-programmed rules, we
let the machines learn from the data,
extract knowledge automatically and then use the
knowledge to perform a complex task.
The knowledge in a machine learning system
is basically some mathematical transformation mapping functions
that are encapsulated in layers.
We talked about shallow learning, meaning
using very small amount of layers. Okay.
This is the knowledge, the brain in the machine
learning system or using deep learning with many layers.
The parameters of those mapping functions
are identified automatically during the training
process by a specific optimization algorithm.
We also talked about the options
to train a machine learning system.
The first very common option
is called supervised learning.
In this method, we need to supervise the
learning process by providing the machine learning algorithm
labeled examples as a training dataset.
This training dataset will be used to train a
model that can be used by an ML system in
a production environment, something that is called inference.Training
a model is a complex process of selecting the
relevant algorithm, tuning the input features, cleaning the data,
testing the performance of the model, trying to overcome
two main challenges underfitting and overfitting,
and the final goal of a trained model is to
be well generalized and to handle new unseen data.
There are two typical tasks that can be
handled under supervised learning classification and regression.
Classification is a very popular use case in supervised
learning and it is used to classify the input
stream like image classifier or spam classifier.
We can build a classifier using shallow
learning or using deep learning algorithm.
The second very common method in
supervised learning is called regression.
It is a very straightforward method to predict
a continuous number based on historical data.
The concept of regression analysis is widely used
for data analysis, but in addition it is
also one of the most basic form of
machine learning. Moving next to unsupervised learning.
One of the main challenges when using supervised
learning is to get labeled dataset because
in most practical application we don't have it,
we will have an unlabeled dataset.
Unsupervised learning is learning without a
teacher supervising the learning process.
The goal is to identify automatically
meaningful patterns in unlabeled data.
It is used for two main
fundamental task clustering and dimension reduction.
Clustering is about summarizing and grouping
similar instances together into clusters okay.
Like cluster number 1, 2, 3, etc.
It is widely used by search
engines, customer segmentation, application image segmentation,
and simple data analysis.
Dimension reduction is about reducing the complexity
of the input data while maintaining the
structure and usefulness of the data.
This method is sometimes used as a
preprocess stage to the data before feeding
it into a supervised learning algorithm.
The third learning option is reinforcement learning.
It is used in applications that the
machine must make a sequence of decisions
while interacting with the outside environment.
Those decisions are coming with positive or negative
consequences, which will be translated by the agent
as a feedback loop to learn what is
working and what is not working.
The cumulative knowledge that the agent is creating
on how to achieve a specific goal is
reinforced again and again based on experience, okay,
the same way as humans are learning from experience.
So those are the three
options of machine learning system.
It was a quick recap to connect the dots. That's it.
I want to thank you for watching this training.
I hope that you enjoyed it and
learned some interesting things along the way.
It will be awesome and useful if you can
can rate the course and share your experience.
I'm planning to create multiple courses
under the concept of machine learning
and also on other interesting topics.
So check out the bonus lecture at the end of
the training and see which courses are already available today.
I hope to see you again in my next training courses.
Bye bye.
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