Malaria parasite detection using ensemble learning in Keras

Offered By
Coursera Project Network
In this Guided Project, you will:

Transform image files into arrays and create datasets

Create and Train a CNN model in Keras

Clock2 hours
IntermediateIntermediate
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

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

Machine LearningPython ProgrammingEnsemble Learningpython CVImage Processing

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:

  1. Load the cell image data

  2. Transform the image files into arrays and create the datasets

  3. Create a deep CNN

  4. Train and test the CNN

  5. Create the CNN models ensemble

  6. Fit the models in the ensemble and perform the prediction

  7. 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

Frequently asked questions

Frequently Asked Questions

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