supervised learning python

Supervised learning algorithms are a type of Machine Learning algorithms that always have known outcomes. Briefly, you know what you are trying to predict.

Related Courses:

• Machine Learning Intro for Python Developers

Supervised Learning Phases
All supervised learning algorithms have a training phase (supervised means ‘to guide’). The algorithm uses training data which is used for future predictions.

The supervised learning process

The supervised learning process always has 3 steps:

• build model (machine learning algorithm)


• train mode (training data used in this phase)


• test model (hypothesis)

Examples
In Machine Learning, an example of supervised learning task is classification. Does an input image belong to class A or class B?

A specific example is ‘face detection’. The training set consists of images containing ‘a face’ and ‘anything else’. Based on this training set a computer may detect a face (more similar to features from one set compared to the other set).

Application of supervised learning algorithms include:

• Financial applications (algorithmic trading)


• Bioscience (detection)


• Pattern recognition (vision and speech)

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