Machine Learning for Engineering & Science Applications | IIT Madras - #41 Multinomial Classification | One Hot Vector

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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22 learners

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

Lecture 41 Inception Module Residual Connection Residual learning Bottleneck Transition Block SegNet Grid search Random Search Bayesian Optimization Transfer Learning CNN as feature extractor

Full Transcript

Welcome to 'Machine Learning for Engineering & Science Applications' course ! This lecture delves into the One-hot vector representation for output classes in multinomial classification. It explains how to construct One-hot vectors for binary and multiclass scenarios, highlighting their role in representing categorical data numerically. The lecture emphasizes the advantages of using One-hot vectors for handling multiple classes in machine learning models. 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 #MultinomialClassification #OneHotVector #OutputRepresentation #BinaryClassification #MulticlassClassification

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