Predictive Analytics - Regression and Classification 6BS2PVYVER

URI	`m2://res/6BS2PVYVER`
Kind	course
Creators	Sourish Das (instructor)
Date	2025-03-18
Scope	in

Expressions

e1: edition

Language	en
Date	2025-03-18

m1: page, Chennai Mathematical Institute (record). nptel 111106164
m2: page, 2025-03-18 (record). nptel 111106164

Contents

part 1: Week 1
session 1: Introduction
session 2: Least Squares method
session 3: Hands-on with Python Part -1
session 4: Hands-on with R Part -1
session 5: Categorical Variable as Predictor Part -1
session 6: Categorical Variable as Predictor Part -2
session 7: Hands-on with R Part -2
session 8: Understanding the joint probability from data perspective
session 9: Hands-on with R Part -3
session 10: Regression Line as Conditional Expectation
part 2: Week 2
session 11: Normal Equations
session 12: Gauss Markov Theorem
session 13: Hands-on with Python Part -2
session 14: Geometry of Regression Model and Feature Engineering
session 15: Sampling Distribution and Statistical Inference of Regression Coefficient
session 16: Hands-on with R Part -4
part 3: Week 3
session 17: Checking Model Assumptions
session 18: Comparing Models with Predictive Accuracy
session 19: Hands-on with Julia
session 20: Model Complexity, Bias and Variance Tradeoff
session 21: Feature Selection, Variable Selection
session 22: Hands on with R Part - 5
part 4: Week 4
session 23: Understanding Multicollinearity
session 24: Ill-Posed Problem and Regularisation, LASSO and Risdge
session 25: Time Series Forecasting with Regression Model
session 26: Hands on with R Part - 6
session 27: Granger Causal model
session 28: Hands on with R Part - 7
part 5: Week 5
session 29: Capital Asset Pricing Model
session 30: Hands on with R for CAPM
session 31: Bootstrap Regression
session 32: Hands on with R for Bootstrap Regression
session 33: Hands on with Python : Handle multicollinearity with Ridge correction
session 34: Hands on with Julia: Implemente Chennai Temperature Analysis with Julia and CRRao
part 6: Week 6
session 35: Introduction to logistic Regression
session 36: Maximum Likelihood Estimate for Logistic Regression
session 37: Hands on with R for Logistic Regression
session 38: Hands on with R : Measure Time performance of R code
session 39: Statistical Inference of Logistic Regression
session 40: Hands on with R with Iris Dataset
session 41: Multi-Class Classification with Discriminant Analysis
session 42: Hands on with R: Implement LDA
part 7: Week 7
session 43: Effect of Feature Engineer in Logistic Regression
session 44: Logistic Regression to Deep Learning Neural Network
session 45: Hands on with R : Feature Engineer in Logistic Regression
session 46: Generalised Linear Model
session 47: Hands on with R : Poisson Regression with Football Data
part 8: Week 8
session 48: Gaussian Process Regression
session 49: Hands on with R: Implement GP Regression from scratch
session 50: Tree Structured Regression
session 51: Hands on with R: Implement Tree Regression and Random Forest with Simulated Data
session 52: Hands on with R: Implement Tree Regression and Random Forest with EPL football Data
part 9: Week 9
session 53: Hands on with Python : Analysis of Bangalore House Price Data
session 54: Hands on with R : Prediction of Bangalore House Price
session 55: Hands on with R : More Prediction of Bangalore House Price
part 10: Week 10
session 56: Hands on with R: Some Correction with Bangalore House Price Data Prediction
session 57: Hands on with R: Classify fake bank note with GLM
session 58: Hands on with R: Dynamic Pricing with Cheese Data
part 11: Week 11
session 59: Hands on with Julia | Bayesian Logistic Regression with Horse Shoe Prior | Genetic Data Analysis
session 60: Hands on with Julia | Bayesian Poisson Regression with Horse Shoe Prior | English Premier League Data
session 61: Why Julia is Future for Data Science Projects?
part 12: Week 12
session 62: Course Review
session 63: Concluding Remarks

Catalogue records

nptel: 111106164 (manifestation)
nptel_video: 111106164 (manifestation)