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#34 OR Gate Via Classification | Machine Learning for Engineering & Science Applications
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Machine Learning for Engineering & Science Applications | IIT Madras - #34 OR Gate Via Classification | 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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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

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Welcome to 'Machine Learning for Engineering & Science Applications' course ! This lecture demonstrates how logistic regression can be used to represent an OR gate as a classification problem. We'll discover how to find the perfect weights for a logistic regression model to accurately classify the four possible inputs of an OR gate. We'll also emphasize the crucial role of the bias unit in logistic regression and the non-uniqueness of weights. 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 #ORGate #Classification #BiasUnit

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