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#93 Introduction to Generative Model | Machine Learning for Engineering & Science Applications
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Machine Learning for Engineering & Science Applications | IIT Madras - #93 Introduction to Generative Model | 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

Summary

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Welcome to 'Machine Learning for Engineering & Science Applications' course ! Tired of just classifying data? Want to create something new? This lecture opens the door to Generative Models, a class of algorithms that can synthesize new data that resembles your training set. We'll contrast generative models with supervised and unsupervised learning, highlighting their unique ability to model the underlying probability distribution of your data. From generating realistic images and text to enhancing resolution and creating synthetic medical data, the applications are mind-blowing! We'll also introduce the stars of generative modeling: Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). Get ready to unleash your creativity! 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 #GenerativeModels #SupervisedLearning #UnsupervisedLearning #DensityEstimation #DeepGenerativeModels #Applications

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