Ai & Machine Learning

Classification Of Machine Learning In Kyphosis Desease

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Coursera

Utilize Sklearn to create decision tree and random forest models for the prediction of kyphosis, with potential applications in healthcare diagnostics.

Key AI Functions:data science, python programming, machine learning, statistical classification, ai & machine learning

Description for Classification Of Machine Learning In Kyphosis Desease

  • 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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