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Hi and welcome back.
In the previous section we had some high level
overview about the topic of artificial intelligence and the
connection to machine learning and deep learning.
We briefly talked about the interesting new
approach of letting machines learn from data,
instead of writing complex rule based programs.
Now we are going to dive a little bit more
trying to uncover the basic terminology of machine learning.
This high level introduction will help us to
develop the ground flow and then we will
be able to build more complex topics
on top of it. I'm going to talk about
the Black Box metaphor that sometimes is used to
describe the machine learning system, the concept of Features,
Labels, and Examples that represent the input and output
of such system, the meaning of a Model and
the lifecycle of a trained model.
And finally, we will also talk about
the main challenges of training a model,
which are called Under fitting and Over fitting.
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