Machine Learning Optimization Using Genetic Algorithm

Dana Knight

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25 Videos (2h 6m)
    • Introduction and Course Scope

      1:48
    • Machine Learning

      7:45
    • Support Vector Machine

      4:39
    • Neural Networks

      11:11
    • Optimization

      5:29
    • Metaheuristics

      2:41
    • Genetic Algorithm #1

      2:35
    • Genetic Algorithm #2

      7:33
    • Genetic Algorithm #3

      4:35
    • Genetic Algorithm #4

      5:08
    • Dataset

      1:54
    • Support Vector Machine Optimization #1

      5:57
    • Support Vector Machine Optimization #2

      7:37
    • Support Vector Machine Optimization #3

      7:42
    • Support Vector Machine Optimization #4

      6:40
    • Support Vector Machine Optimization #5

      6:28
    • Support Vector Machine Optimization #6

      5:58
    • Support Vector Machine Optimization #7

      7:31
    • Multilayer Perceptron Neural Network Optimization #1

      3:09
    • Multilayer Perceptron Neural Network Optimization #2

      4:39
    • Multilayer Perceptron Neural Network Optimization #3

      5:16
    • Multilayer Perceptron Neural Network Optimization #4

      6:25
    • Feature Selection #1

      3:28
    • Feature Selection #2

      5:19
    • Feature Selection #3

      7:00

About This Class

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 a regression dataset for the prediction of cooling and heating loads of buildings. The SVM and MLP will be applied on the dataset 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.

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Hi! I'm Dana. I'm currently a PhD student in Industrial Engineering. I finished my B.S. in Architectural Engineering and my M.S. in Industrial Engineering. Lean Six Sigma Green Belt certified. I enjoy learning new things. My research interest is Data Science including Deep Learning, Machine Learning, and Artificial Intelligence.

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