Machine Learning for Engineering & Science Applications | IIT Madras - #23 The Learning Paradigm | 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!

4.0 (2)

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

The Learning Paradigm Lecture 23 20 31 01 19 Part 01

Full Transcript

Welcome to 'Machine Learning for Engineering & Science Applications' course ! This lecture delves into the fundamental learning paradigm used in deep learning. It explains the core concepts, including feedforward and feedback mechanisms, the role of models and hypotheses, and the significance of ground truth data. Additionally, it emphasizes the crucial role of optimization and hyperparameters in training deep 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 #DeepLearning #LearningParadigm #FeedForward #Feedback #Model #Hypothesis #GroundTruth #Optimization #Hyperparameters

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