#32 Logistic Regression | Machine Learning for Engineering & Science Applications

Machine Learning for Engineering & Science Applications | IIT Madras - #32 Logistic Regression | Machine Learning for Engineering & Science Applications

Unlock the Future: Master AI & Machine Learning with NPTEL-IITM’s Comprehensive Course! Dive into Neural Networks, Deep Learning, Probabilities, and Optimization Techniques tailored for Engineering & Science Applications. Your AI journey starts here!

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What you'll learn

Understand the historical development and foundational concepts of artificial intelligence.
Gain proficiency in applying machine learning techniques to engineering and science problems.
Develop skills in using linear algebra and calculus for machine learning modeling.
Learn to implement and optimize machine learning algorithms using Python packages.

This course includes

32 hours of video
Certificate of completion
Access on mobile and TV

Summary

Keywords

Logistic Regression Lecture 32

Full Transcript

Welcome to 'Machine Learning for Engineering & Science Applications' course ! This lecture introduces logistic regression, a classification algorithm used for binary classification tasks. It explains how logistic regression differs from linear regression and discusses the use of the sigmoid function to model the probability of belonging to a particular class. The lecture emphasizes the application of logistic regression in creating predictive models for classifying data into two categories. NPTEL Courses permit certifications that can be used for Course Credits in Indian Universities as per the UGC and AICTE notifications. To understand various certification options for this course, please visit https://nptel.ac.in/courses/106106198 #LogisticRegression #Classification #BinaryClassification #SigmoidFunction #PredictiveModel