This course teaches the concepts of Logistic Regression and Naive Bayes in Machine Learning. The learning outcomes include understanding the need for Machine Learning, the differences between traditional programming and Machine Learning, the Machine Learning lifecycle, types of Machine Learning, supervised learning, Logistic Regression, and Naive Bayes classification. The course demonstrates the application of these concepts through examples like credit card fraud detection and diabetes prediction. The intended audience for this course is individuals interested in expanding their knowledge of Machine Learning algorithms and their practical applications.
Logistic Regression and Naive Bayes in Machine Learning - Machine Learning Tutorial - Great Learning
Overview
Syllabus
➤ Skip Intro: .
Introduction.
Agenda.
Why do we need Machine Learning?.
What is Machine Learning?.
Traditional programming vs Machine Learning.
Machine Learning lifecycle.
Types of Machine Learning.
What is supervised learning?.
What is Logistic Regression?.
Credit card fraud detection demo.
Introduction Naive Bayes classification.
Example of Bayes Theorem.
Diabetes prediction using Naive Bayes.
Summary.
Taught by
Great Learning
