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Hi and welcome back.

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In the previous section we had some high level

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overview about the topic of artificial intelligence and the

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connection to machine learning and deep learning.

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We briefly talked about the interesting new

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approach of letting machines learn from data,

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instead of writing complex rule based programs.

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Now we are going to dive a little bit more

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trying to uncover the basic terminology of machine learning.

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This high level introduction will help us to

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develop the ground flow and then we will

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be able to build more complex topics

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on top of it. I'm going to talk about

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the Black Box metaphor that sometimes is used to

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describe the machine learning system, the concept of Features,

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Labels, and Examples that represent the input and output

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of such system, the meaning of a Model and

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the lifecycle of a trained model.

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And finally, we will also talk about

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the main challenges of training a model,

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which are called Under fitting and Over fitting.

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