Machine Learning for Engineering & Science Applications | IIT Madras - #25 Linear Regression | Least Squares | Gradient Descent

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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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

Linear Regression Least Squares Gradient Descent Lecture 25 Lecture 25 21 04 02 19

Full Transcript

Welcome to 'Machine Learning for Engineering & Science Applications' course ! This lecture delves into the details of linear regression, explaining the principles of least squares fitting and gradient descent optimization. It demonstrates how to derive the coefficients for linear models and discusses the process of systematically finding the best fit for given data. The lecture emphasizes understanding the underlying mechanisms of parameter estimation in linear regression. 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 #LinearRegression #LeastSquares #GradientDescent #ModelFitting #ParameterEstimation