Supervised learning algorithms are a type of Machine Learning algorithms that always have known outcomes. Briefly, you know what you are trying to predict.
- Machine Learning Intro for Python Developers
- Introduction to Machine Learning & Face Detection in Python
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 always has 3 steps:
- build model (machine learning algorithm)
- train mode (training data used in this phase)
- test model (hypothesis)
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)