Malaria parasite detection using ensemble learning in Keras
Transform image files into arrays and create datasets
Create and Train a CNN model in Keras
In this 1-hour long project-based course, you will learn what ensemble learning is and how to implement is using python. You will create deep convolutional neural networks using the Keras library to predict the malaria parasite. You will learn various ways of assessing classification models. You will create an ensemble of deep convolutional neural networks and apply voting in order to combine the best predictions of your models. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Skills you will develop
Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Load the cell image data
Transform the image files into arrays and create the datasets
Create a deep CNN
Train and test the CNN
Create the CNN models ensemble
Fit the models in the ensemble and perform the prediction
Apply hard voting to the ensemble
How Guided Projects work
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
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