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Supervised Learning with Python

Supervised learning algorithms play a crucial role in Machine Learning, focusing on predicting known outcomes. Simply put, with supervised learning, the objective of prediction is predetermined.

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Stages of Supervised Learning
Supervised learning, as its name suggests, is about guiding the model during training. There’s a specific sequence that these algorithms follow:

The supervised learning process

The standard supervised learning workflow consists of:

  1. Constructing the model using a specific machine learning algorithm.
  2. Training the model using a set of training data.
  3. Testing the model’s hypothesis and predictions.

Common Use-Cases
Within the domain of Machine Learning, supervised learning often finds application in classification tasks. For instance, determining whether an input image falls under class A or class B.

A practical scenario where supervised learning shines is in face detection. Here, the training dataset comprises images labeled either as ‘a face’ or ‘not a face’. Using these labels, computers can identify faces by comparing the similarity of features from the two sets.

Some popular applications of supervised learning algorithms are:

  • Algorithmic trading in the financial sector.
  • Detection tasks in bioscience.
  • Vision and speech pattern recognition.

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