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In the previous section you were introduced to the basics of machine learning, you were introduced to the simple linear regression and regularized linear regression models.

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Linear methods are the most used model in finance, and in this section you will investigate more types of linear models.

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After that we will look into non-linear regression models like KNN regression models (k-nearest neighbors algorithm).

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We will not yet look at neural networks here, that would be left for another lecture.

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Will not investigate tree and rule-based models here — these would be addressed in the classification section.

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Many models can be used both in classification and regression tasks and sometimes just require one simple mathematical switch.

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The purpose of this section is to give you a scope of how many different forms of regression models are available to us.

Linear Model Innovations | Purpose | Difficulty |

Generalized Linear Model | Model to predict alternative values (count, categories, survival, skewed value) of different distributions as opposed to just continuous values with normal distributions. | Medium |

Augmented Linear Model | Model with a preprocessing step to generate or transform features and labels to improve the fit of the model. | Easy |

Penalized Augmentation Model | An automated method to augment the data as part of the modelling step while penalizing excessive feature creation. | Hard |

Sampled Linear Models | A method to adjust the importance of different samples (instances) based on some criteria e.g., old data should be less important than recent data. | Medium |

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