Curiosity for Data Science
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
Instructor Details
Courses : 2
Specification: Machine Learning Optimization Using Genetic Algorithm
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11 reviews for Machine Learning Optimization Using Genetic Algorithm
Add a review
| Price | $17.99 |
|---|---|
| Provider | |
| Duration | 6.5 hours |
| Year | 2020 |
| Level | Intermediate |
| Language | English |
| Certificate | Yes |
| Quizzes | Yes |
$17.99
Mason Baran –
One of the most thorough and well explained tutorials I could find on Genetic Algorithms!
Jeffrey Stanford Scruggs –
This is a really good tutorial! Thank you!
Oscar Omar Estrada –
I had a little background in machine learning. This course help me complement that knowledge.
Saurabh Tewari –
Good lectures on such as specific topics
M. Rakhmat Setiawan –
Thank you Dana, for your insightful information and demonstration on how we can use metaheuristics to optimize our machine learning model. As a intermediate level on Data science, I found out that this lecture has no problem with me since I can modify your resource code if there is problem inside the code. For example, there is a problem on cross validation part. Thus, I can change it by importing from sklearn.model selection import train test split. However, as a newbie in data science and python, I suggested you to fix the code to a new version of python so that everyone who has lack experience in data science and python programming can catch up your point. Keep up your good work, Dana!
Steven Miller –
Very good course. I like to see the actual code for the algorithm
Nedumal –
The course served as a platform knowing the GA, SVM,NN in python. Thank you
George Sidiras –
Vary Basic/ Introduction lesson to learn the basic
Arion Hardison –
Volume
Abrahan Barriga –
Well explained
Andreas Nittke –
Hauptkritikpunkte: unendliche Wiederholungen Programmieren in Python im BASIC Stil der 80er Jahre und auf Anf ngerniveau Dadurch ist es schwierig, den roten Faden nicht zu verlieren.