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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

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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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