Machine Learning Optimization Using Genetic Algorithm

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 (SVMs) and Multilayer Perceptron Neural Networks (MLP NNs). It is referred to as hyperparameter tuning or parameter tuning.

You will also learn how to do feature selection using Genetic Algorithm. Hyperparameter optimization will be done on two datasets:A regression dataset for the prediction of cooling and heating loads of buildingsA classification dataset regarding the classification of emails into spam and non-spamThe SVM and MLP will be applied on the datasets without optimization and compare their results to after their optimizationFeature Selection will be done on one dataset:Classification of benign tumors from malignant tumors in a breast cancer datasetBy 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 maximum performance. You would have also learnt how to apply Genetic Algorithm for feature selection.

To sum up:You will learn what hyperparameters are (sometimes referred to as parameters, though different)You will learn Genetic AlgorithmYou will use Genetic Algorithm to optimize the performance of your machine learning algorithmsMaximize your model's accuracy and predictive abilitiesOptimize the performance of SVMs and MLP Neural NetworksApply feature selection to extract the features that are relevant to the predicted outputGet the best out of your machine learning modelRemove redundant features, which in return will reduce the time and complexity of your modelUnderstand what are the features that have a relationship to the output and which do notYou do not need to have a lot of knowledge and experience in optimization or Python programming - it helps, but not a must to succeed in this course. This course will teach you how to optimize the functionality of your machine learning algorithmsWhere every single line of code is explained thoroughlyThe code is written in a simple manner that you will understand how things work and how to code Genetic Algorithm even with zero knowledge in PythonBasically, you can think of this as not only a course that teaches you how to optimize your machine learning model, but also Python programming. Please feel free to ask me any question.

Don't like the course. Ask for a 30-day refund. Real Testaments ->1) "This is my second course with Dana.