Practical Machine Learning

Practical Machine Learning

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Learn to construct and implement prediction functions, understand overfitting and error rates, and grasp machine learning techniques like classification trees and regression.

Key AI Functions:Random Forest,Machine Learning (ML) Algorithms,Machine Learning,R Programming

Description for Practical Machine Learning

Features of Course

  • Utilize the fundamental components of constructing and implementing prediction functions.
  • Comprehend the concepts of overfitting, error rates, and training and test sets.
  • Define machine learning techniques, including classification trees and regression.
  • Describe the entire process of developing prediction functions.
  • Level: Intermediate

    Certification Degree: Yes

    Languages the Course is Available: 22

    Offered by: On Coursera provided by Johns Hopkins University

    Duration: 8 hours (approximately)

    Schedule: Flexible

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