Introduction to Bayesian Linear Regression statistics | NerdML

Bayesian Linear Regression - This video will help you to understand the drawbacks of solving Linear Regression problems using Least Square Error or Gradient Descent over Statistical approach of Bayesian Learning. I address the question of why a Bayesian approach is preferable to using the MLE or MAP estimate. We use a coin toss experiment to demonstrate the idea of prior probability, likelihood functions, posterior probabilities, posterior means and probabilities as well as credible intervals. Derived mathematical functions of Bayesian learning. Below topics are explained in this video: 1. Frequentist vs Bayesian approach? ( 00:15 ) 2. Case Study on Frequentist approach ( 00:55 ) 3. Bayesian Approach over Frequentist Method for solving Linear Regression problems ( 05:07 ) 4. Why we need to use Bayesian Learning for solving Linear Regression problem (05:49) 5. Linear Regression solution using Bayesian Learning (08:14) Do subscribe to my channel and hit the bell icon to never miss an update in the future: https://www.youtube.com/channel/UC7tzG9dDMcp0-WfGROT9cYw/ Please find the previous Video link - An Introduction to Simple Linear Regression Analysis - Gradient Descent | NerdML : https://youtu.be/k7lieXqeR1s Machine Learning Tutorial Playlist: https://youtube.com/playlist?list=PLAH6DbJL1J2KroCzEWRF0xnrmet9esxFH Deep Learning Tutorial Playlist : https://youtube.com/playlist?list=PLAH6DbJL1J2KK-5PlYCt3v2PaIGdvuLmB Prerequisites Basic understanding of Linear Algebra, Probability, Matrix & Python programming including pandas, numpy, scikit learn & some visualization tools. ------------------------------------------------------ Creator : Rahul Saini Please write back to me at [email protected] for more information Instagram: https://www.instagram.com/96_saini Facebook: https://www.facebook.com/rahulsainipusa LinkedIn: https://www.linkedin.com/in/rahul-s-22ba1993 #BayesianLinearRegression, #BayesianLearning, #NerdML, #MachineLearning, #SimpleLinearRegression, #LinearRegression, #Statistics, #Mathematics, #Probability, #Priors, #PosteriorProbability, #StandardDeviation, #Mean, #NormalDistribution, #HalfCauchyDistribution, #LeastSquareError, #GradientDescent, #Likelihood, #BestFitLine