Machine Learning for Kyphosis Disease Classification
Utilize Sklearn to create decision tree and random forest models for the prediction of kyphosis, with potential applications in healthcare diagnostics.
Description for Machine Learning for Kyphosis Disease Classification
Features of the Course:
Decision Trees and Random Forest Classifiers: Comprehend the fundamental theory and intuition of decision trees and random forest classifiers, which are indispensable for precise predictive modeling.
Sklearn Model Building: Implement Python's Sklearn library to acquire practical experience in the development, training, and testing of decision tree and random forest models.
Feature Engineering and Data Cleaning: Execute critical data cleansing, feature engineering, and data visualization techniques to enhance the accuracy of the model.
Application in the Healthcare Sector: This endeavor is both practical and industry-relevant by utilizing machine learning techniques to predict kyphosis, a healthcare condition.
Level: Beginner
Certification Degree: Yes
Languages the Course is Available: 1
Offered by: On Coursera provided by Coursera Project Network
Duration: 2 hours at your own pace
Schedule: Hands-on learning
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