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Description

In this course, you will learn what hyperparameters are, what Genetic Algorithm is, and what hyperparameter optimization is. In this course, you will apply Genetic Algorithm to optimize the performance of Support Vector Machines and Multilayer Perceptron Neural Networks. Hyperparameter optimization will be done on two datasets, a regression dataset for the prediction of cooling and heating loads of buildings, and a classification dataset regarding the classification of emails into spam and non-spam. The SVM and MLP will be applied on the datasets without optimization and compare their results to after their optimization.

By the end of this course, you will have learnt how to code Genetic Algorithm in Python and how to optimize your Machine Learning algorithms for maximal performance. The ideal student is someone with some knowledge in Machine Learning algorithms and some prior knowledge in optimization, some prior knowledge in coding will help too.

Please feel free to ask me any question! Don’t like the course? Ask for a 30-day refund!!

Who is the target audience?
  • Anyone who wants to learn Genetic Algorithm
  • Anyone who would like to optimize the functionality of their Machine Learning algorithms