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.
Malaria parasite detection using ensemble learning in Keras
199 already enrolled
What you'll learn
Transform image files into arrays and create datasets
Create and Train a CNN model in Keras
Skills you'll practice
- Category: Machine Learning
- Category: Python Programming
- Category: Ensemble Learning
- Category: python CV
- Category: Image Processing
Details to know
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Learn, practice, and apply job-ready skills in less than 2 hours
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About this Guided Project
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
At least 1 year experience with python and a basic understanding of convolutional neural networks
4 project images
How you'll learn
Skill-based, hands-on learning
Practice new skills by completing job-related tasks.
Follow along with pre-recorded videos from experts using a unique side-by-side interface.
No downloads or installation required
Access the tools and resources you need in a pre-configured cloud workspace.
Available only on desktop
This Guided Project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.
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