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#33 Binary Entropy Cost Function | Machine Learning for Engineering & Science Applications
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Machine Learning for Engineering & Science Applications | IIT Madras - #33 Binary Entropy Cost Function | 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 focuses on the binary entropy cost function used in logistic regression. It explains how this cost function quantifies the error in the model's predictions and guides the optimization process during model training. The lecture aims to provide a deeper understanding of the role of cost functions in optimizing logistic regression 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 #BinaryEntropy #CostFunction #LogisticRegression #Optimization #ModelTraining

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