Malaria parasite detection using ensemble learning in Keras

Offered By
In this Guided Project, you will:

Transform image files into arrays and create datasets

Create and Train a CNN model in Keras

2 hours
Intermediate
No download needed
Split-screen video
English
Desktop 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 Learning

  • Python Programming

  • Ensemble Learning

  • python CV

  • Image 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

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Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.

You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.