| Sl.No | Chapter Name | MP4 Download |
|---|---|---|
| 1 | Introduction | Download |
| 2 | Least Squares method | Download |
| 3 | Hands-on with Python Part -1 | Download |
| 4 | Hands-on with R Part -1 | Download |
| 5 | Categorical Variable as Predictor Part -1 | Download |
| 6 | Categorical Variable as Predictor Part -2 | Download |
| 7 | Hands-on with R Part -2 | Download |
| 8 | Understanding the joint probability from data perspective | Download |
| 9 | Hands-on with R Part -3 | Download |
| 10 | Regression Line as Conditional Expectation | Download |
| 11 | Normal Equations | Download |
| 12 | Gauss Markov Theorem | Download |
| 13 | Hands-on with Python Part -2 | Download |
| 14 | Geometry of Regression Model and Feature Engineering | Download |
| 15 | Sampling Distribution and Statistical Inference of Regression Coefficient | Download |
| 16 | Hands-on with R Part -4 | Download |
| 17 | Checking Model Assumptions | Download |
| 18 | Comparing Models with Predictive Accuracy | Download |
| 19 | Hands-on with Julia | Download |
| 20 | Model Complexity, Bias and Variance Tradeoff | Download |
| 21 | Feature Selection, Variable Selection | Download |
| 22 | Hands on with R Part - 5 | Download |
| 23 | Understanding Multicollinearity | Download |
| 24 | Ill-Posed Problem and Regularisation, LASSO and Risdge | Download |
| 25 | Hands-on with Python Part -3 | Download |
| 26 | Time Series Forecasting with Regression Model | Download |
| 27 | Hands on with R Part - 6 | Download |
| 28 | Granger Causal model | Download |
| 29 | Hands on with R Part - 7 | Download |
| 30 | Capital Asset Pricing Model | Download |
| 31 | Hands on with R for CAPM | Download |
| 32 | Bootstrap Regression | Download |
| 33 | Hands on with R for Bootstrap Regression | Download |
| 34 | Hands on with Python : Handle multicollinearity with Ridge correction | Download |
| 35 | Hands on with Julia: Implemente Chennai Temperature Analysis with Julia and CRRao | Download |
| 36 | Introduction to logistic Regression | Download |
| 37 | Maximum Likelihood Estimate for Logistic Regression | Download |
| 38 | Hands on with R for Logistic Regression | Download |
| 39 | Hands on with R : Measure Time performance of R code | Download |
| 40 | Statistical Inference of Logistic Regression | Download |
| 41 | Hands on with R with Iris Dataset | Download |
| 42 | Multi-Class Classification with Discriminant Analysis | Download |
| 43 | Hands on with R: Implement LDA | Download |
| 44 | Effect of Feature Engineer in Logistic Regression | Download |
| 45 | Logistic Regression to Deep Learning Neural Network | Download |
| 46 | Hands on with R : Feature Engineer in Logistic Regression | Download |
| 47 | Generalised Linear Model | Download |
| 48 | Hands on with R : Poisson Regression with Football Data | Download |
| 49 | Gaussian Process Regression | Download |
| 50 | Hands on with R: Implement GP Regression from scratch | Download |
| 51 | Tree Structured Regression | Download |
| 52 | Hands on with R: Implement Tree Regression and Random Forest with Simulated Data | Download |
| 53 | Hands on with R: Implement Tree Regression and Random Forest with EPL football Data | Download |
| 54 | Hands on with Python : Analysis of Bangalore House Price Data | Download |
| 55 | Hands on with R : Prediction of Bangalore House Price | Download |
| 56 | Hands on with R : More Prediction of Bangalore House Price | Download |
| 57 | Hands on with R: Some Correction with Bangalore House Price Data Prediction | Download |
| 58 | Hands on with R: Classify fake bank note with GLM | Download |
| 59 | Hands on with R: Dynamic Pricing with Cheese Data | Download |
| 60 | Hands on with Julia | Bayesian Logistic Regression with Horse Shoe Prior | Genetic Data Analysis | Download |
| 61 | Hands on with Julia | Bayesian Poisson Regression with Horse Shoe Prior English Premier League Data | Download |
| 62 | Why Julia is Future for Data Science Projects? | Download |
| 63 | Course Review | Download |
| 64 | Concluding Remarks | Download |
| Sl.No | Chapter Name | English |
|---|---|---|
| 1 | Introduction | PDF unavailable |
| 2 | Least Squares method | PDF unavailable |
| 3 | Hands-on with Python Part -1 | PDF unavailable |
| 4 | Hands-on with R Part -1 | PDF unavailable |
| 5 | Categorical Variable as Predictor Part -1 | PDF unavailable |
| 6 | Categorical Variable as Predictor Part -2 | PDF unavailable |
| 7 | Hands-on with R Part -2 | PDF unavailable |
| 8 | Understanding the joint probability from data perspective | PDF unavailable |
| 9 | Hands-on with R Part -3 | PDF unavailable |
| 10 | Regression Line as Conditional Expectation | PDF unavailable |
| 11 | Normal Equations | PDF unavailable |
| 12 | Gauss Markov Theorem | PDF unavailable |
| 13 | Hands-on with Python Part -2 | PDF unavailable |
| 14 | Geometry of Regression Model and Feature Engineering | PDF unavailable |
| 15 | Sampling Distribution and Statistical Inference of Regression Coefficient | PDF unavailable |
| 16 | Hands-on with R Part -4 | PDF unavailable |
| 17 | Checking Model Assumptions | PDF unavailable |
| 18 | Comparing Models with Predictive Accuracy | PDF unavailable |
| 19 | Hands-on with Julia | PDF unavailable |
| 20 | Model Complexity, Bias and Variance Tradeoff | PDF unavailable |
| 21 | Feature Selection, Variable Selection | PDF unavailable |
| 22 | Hands on with R Part - 5 | PDF unavailable |
| 23 | Understanding Multicollinearity | PDF unavailable |
| 24 | Ill-Posed Problem and Regularisation, LASSO and Risdge | PDF unavailable |
| 25 | Hands-on with Python Part -3 | PDF unavailable |
| 26 | Time Series Forecasting with Regression Model | PDF unavailable |
| 27 | Hands on with R Part - 6 | PDF unavailable |
| 28 | Granger Causal model | PDF unavailable |
| 29 | Hands on with R Part - 7 | PDF unavailable |
| 30 | Capital Asset Pricing Model | PDF unavailable |
| 31 | Hands on with R for CAPM | PDF unavailable |
| 32 | Bootstrap Regression | PDF unavailable |
| 33 | Hands on with R for Bootstrap Regression | PDF unavailable |
| 34 | Hands on with Python : Handle multicollinearity with Ridge correction | PDF unavailable |
| 35 | Hands on with Julia: Implemente Chennai Temperature Analysis with Julia and CRRao | PDF unavailable |
| 36 | Introduction to logistic Regression | PDF unavailable |
| 37 | Maximum Likelihood Estimate for Logistic Regression | PDF unavailable |
| 38 | Hands on with R for Logistic Regression | PDF unavailable |
| 39 | Hands on with R : Measure Time performance of R code | PDF unavailable |
| 40 | Statistical Inference of Logistic Regression | PDF unavailable |
| 41 | Hands on with R with Iris Dataset | PDF unavailable |
| 42 | Multi-Class Classification with Discriminant Analysis | PDF unavailable |
| 43 | Hands on with R: Implement LDA | PDF unavailable |
| 44 | Effect of Feature Engineer in Logistic Regression | PDF unavailable |
| 45 | Logistic Regression to Deep Learning Neural Network | PDF unavailable |
| 46 | Hands on with R : Feature Engineer in Logistic Regression | PDF unavailable |
| 47 | Generalised Linear Model | PDF unavailable |
| 48 | Hands on with R : Poisson Regression with Football Data | PDF unavailable |
| 49 | Gaussian Process Regression | PDF unavailable |
| 50 | Hands on with R: Implement GP Regression from scratch | PDF unavailable |
| 51 | Tree Structured Regression | PDF unavailable |
| 52 | Hands on with R: Implement Tree Regression and Random Forest with Simulated Data | PDF unavailable |
| 53 | Hands on with R: Implement Tree Regression and Random Forest with EPL football Data | PDF unavailable |
| 54 | Hands on with Python : Analysis of Bangalore House Price Data | PDF unavailable |
| 55 | Hands on with R : Prediction of Bangalore House Price | PDF unavailable |
| 56 | Hands on with R : More Prediction of Bangalore House Price | PDF unavailable |
| 57 | Hands on with R: Some Correction with Bangalore House Price Data Prediction | PDF unavailable |
| 58 | Hands on with R: Classify fake bank note with GLM | PDF unavailable |
| 59 | Hands on with R: Dynamic Pricing with Cheese Data | PDF unavailable |
| 60 | Hands on with Julia | Bayesian Logistic Regression with Horse Shoe Prior | Genetic Data Analysis | PDF unavailable |
| 61 | Hands on with Julia | Bayesian Poisson Regression with Horse Shoe Prior English Premier League Data | PDF unavailable |
| 62 | Why Julia is Future for Data Science Projects? | PDF unavailable |
| 63 | Course Review | PDF unavailable |
| 64 | Concluding Remarks | PDF unavailable |
| Sl.No | Language | Book link |
|---|---|---|
| 1 | English | Not Available |
| 2 | Bengali | Not Available |
| 3 | Gujarati | Not Available |
| 4 | Hindi | Not Available |
| 5 | Kannada | Not Available |
| 6 | Malayalam | Not Available |
| 7 | Marathi | Not Available |
| 8 | Tamil | Not Available |
| 9 | Telugu | Not Available |