Machine Learning Optimization Using Genetic Algorithm
What you'll learn
- Apply Genetic Algorithm to optimize Machine Learning algorithms
- Apply Genetic Algorithm on Support Vector Machines and Multilayer Perceptron Neural Networks
- Apply Genetic Algorithm for Feature Selection
- Learn how to code Genetic Algorithm in Python from scratch
- Basic knowledge in Machine Learning
- Basic knowledge in Operations Research and Optimization - (not a must, but helpful)
- Basic programming skills in Python - (not a must, but helpful)
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 buildings
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
Feature Selection will be done on one dataset:
Classification of benign tumors from malignant tumors in a breast cancer dataset
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 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 Algorithm
You will use Genetic Algorithm to optimize the performance of your machine learning algorithms
Maximize your model's accuracy and predictive abilities
Optimize the performance of SVMs and MLP Neural Networks
Apply feature selection to extract the features that are relevant to the predicted output
Get the best out of your machine learning model
Remove redundant features, which in return will reduce the time and complexity of your model
Understand what are the features that have a relationship to the output and which do not
You 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 algorithms
Where every single line of code is explained thoroughly
The 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 Python
Basically, 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. This course is a combination of Metaheuristic and machine learning. It gives a wide picture of machine learning hyperparameter optimization. I recommend taking this course if you know basics of machine learning and you want to solve some problems using ML. By applying the techniques of GA optimization, you will have better performance of ML. The codes provided in this course are very straightforward and easy to understand. The course deserves five stars because of the lecture contents and examples. The instructor knowledgeable about the topic and talented in programming." -- Abdulaziz, 5 star rating
2) "An excellent course! Great for anyone interested in fine-tuning their machine-learning models. I really enjoyed the from scratch implementations and how well they are explained. These implementations from scratch help one understand the theory very well. An interesting thing to point out is that this course uses Metaheustistics to optimise machine-learning. However, you can use machine-learning classifiers to help your Metaheuristic predict good or bad regions." -- Dylan, 5 star rating
3) "Very helpful, for application of optimization algorithm to optimize ML algorithm parameters and got to do this using python, wonderful." -- Erigits, 5 star rating
4) "well explained course. The topic is not an easy one but to date the explanations have been clear. The course has an interesting spreadsheet project." -- Martin, 5 star rating
5) "Thank you very much for this awesome course. Lots of new things learn from this course." -- Md. Mahmudul, 5 star rating
Who this course is for:
- Anyone who wants to learn Genetic Algorithm
- Anyone who would like to optimize the functionality of their Machine Learning algorithms
- Anyone who would like to learn feature selection
- Anyone who wants to code Genetic Algorithm in Python
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 Optimization and Data Science including Deep Learning, Machine Learning, and Artificial Intelligence. My areas of expertise include Python Programming, Data Science, Machine Learning, and Optimization using Metaheuristics.