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[UdemyCourseDownloader] Regression Analysis for Statistics & Machine Learning in R

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English

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1.06 GB

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1e0037737161223371f2a10467a8808b163b88c3

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kirat1h

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13-02-2019 11:43

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File	Size
1. Get Started with Practical Regression Analysis in R/1. INTRODUCTION TO THE COURSE The Key Concepts and Software Tools.mp4	7.78 MB
1. Get Started with Practical Regression Analysis in R/1. INTRODUCTION TO THE COURSE The Key Concepts and Software Tools.vtt	1.78 kB
1. Get Started with Practical Regression Analysis in R/2. Data For the Course.html	151.00 B
1. Get Started with Practical Regression Analysis in R/3. Difference Between Statistical Analysis & Machine Learning.mp4	13.70 MB
1. Get Started with Practical Regression Analysis in R/3. Difference Between Statistical Analysis & Machine Learning.vtt	6.30 kB
1. Get Started with Practical Regression Analysis in R/4. Getting Started with R and R Studio.mp4	18.35 MB
1. Get Started with Practical Regression Analysis in R/4. Getting Started with R and R Studio.vtt	6.56 kB
1. Get Started with Practical Regression Analysis in R/5. Reading in Data with R.mp4	42.59 MB
1. Get Started with Practical Regression Analysis in R/5. Reading in Data with R.vtt	15.18 kB
1. Get Started with Practical Regression Analysis in R/6. Data Cleaning with R.mp4	40.54 MB
1. Get Started with Practical Regression Analysis in R/6. Data Cleaning with R.vtt	15.97 kB
1. Get Started with Practical Regression Analysis in R/7. Some More Data Cleaning with R.mp4	22.87 MB
1. Get Started with Practical Regression Analysis in R/7. Some More Data Cleaning with R.vtt	8.71 kB
1. Get Started with Practical Regression Analysis in R/8. Basic Exploratory Data Analysis in R.mp4	48.24 MB
1. Get Started with Practical Regression Analysis in R/8. Basic Exploratory Data Analysis in R.vtt	19.04 kB
1. Get Started with Practical Regression Analysis in R/8.1 EDA.txt.txt	1.09 kB
1. Get Started with Practical Regression Analysis in R/9. Conclusion to Section 1.mp4	4.65 MB
1. Get Started with Practical Regression Analysis in R/9. Conclusion to Section 1.vtt	2.40 kB
1. Get Started with Practical Regression Analysis in R/Regression Analysis_Data and Scripts.zip	934.86 kB
2. Ordinary Least Square Regression Modelling/1. OLS Regression- Theory.mp4	24.87 MB
2. Ordinary Least Square Regression Modelling/1. OLS Regression- Theory.vtt	10.61 kB
2. Ordinary Least Square Regression Modelling/10. Multiple Linear regression with Interaction and Dummy Variables.mp4	37.54 MB
2. Ordinary Least Square Regression Modelling/10. Multiple Linear regression with Interaction and Dummy Variables.vtt	15.93 kB
2. Ordinary Least Square Regression Modelling/11. Some Basic Conditions that OLS Models Have to Fulfill.mp4	30.50 MB
2. Ordinary Least Square Regression Modelling/11. Some Basic Conditions that OLS Models Have to Fulfill.vtt	13.43 kB
2. Ordinary Least Square Regression Modelling/12. Conclusions to Section 2.mp4	7.00 MB
2. Ordinary Least Square Regression Modelling/12. Conclusions to Section 2.vtt	3.41 kB
2. Ordinary Least Square Regression Modelling/2. OLS-Implementation.mp4	21.59 MB
2. Ordinary Least Square Regression Modelling/2. OLS-Implementation.vtt	9.05 kB
2. Ordinary Least Square Regression Modelling/3. More on Result Interpretations.mp4	17.05 MB
2. Ordinary Least Square Regression Modelling/3. More on Result Interpretations.vtt	8.99 kB
2. Ordinary Least Square Regression Modelling/4. Confidence Interval-Theory.mp4	13.71 MB
2. Ordinary Least Square Regression Modelling/4. Confidence Interval-Theory.vtt	6.23 kB
2. Ordinary Least Square Regression Modelling/5. Calculate the Confidence Interval in R.mp4	10.24 MB
2. Ordinary Least Square Regression Modelling/5. Calculate the Confidence Interval in R.vtt	4.94 kB
2. Ordinary Least Square Regression Modelling/6. Confidence Interval and OLS Regressions.mp4	18.56 MB
2. Ordinary Least Square Regression Modelling/6. Confidence Interval and OLS Regressions.vtt	8.12 kB
2. Ordinary Least Square Regression Modelling/7. Linear Regression without Intercept.mp4	8.26 MB
2. Ordinary Least Square Regression Modelling/7. Linear Regression without Intercept.vtt	3.92 kB
2. Ordinary Least Square Regression Modelling/8. Implement ANOVA on OLS Regression.mp4	7.31 MB
2. Ordinary Least Square Regression Modelling/8. Implement ANOVA on OLS Regression.vtt	3.76 kB
2. Ordinary Least Square Regression Modelling/9. Multiple Linear Regression.mp4	14.94 MB
2. Ordinary Least Square Regression Modelling/9. Multiple Linear Regression.vtt	7.00 kB
3. Deal with Multicollinearity in OLS Regression Models/1. Identify Multicollinearity.mp4	38.18 MB
3. Deal with Multicollinearity in OLS Regression Models/1. Identify Multicollinearity.vtt	16.68 kB
3. Deal with Multicollinearity in OLS Regression Models/1.1 Lecture21_multicol1.txt.txt	1.77 kB
3. Deal with Multicollinearity in OLS Regression Models/2. Doing Regression Analyses with Correlated Predictor Variables.mp4	12.81 MB
3. Deal with Multicollinearity in OLS Regression Models/2. Doing Regression Analyses with Correlated Predictor Variables.vtt	6.58 kB
3. Deal with Multicollinearity in OLS Regression Models/3. Principal Component Regression in R.mp4	25.46 MB
3. Deal with Multicollinearity in OLS Regression Models/3. Principal Component Regression in R.vtt	11.23 kB
3. Deal with Multicollinearity in OLS Regression Models/4. Partial Least Square Regression in R.mp4	17.37 MB
3. Deal with Multicollinearity in OLS Regression Models/4. Partial Least Square Regression in R.vtt	7.80 kB
3. Deal with Multicollinearity in OLS Regression Models/5. Ridge Regression in R.mp4	18.53 MB
3. Deal with Multicollinearity in OLS Regression Models/5. Ridge Regression in R.vtt	7.70 kB
3. Deal with Multicollinearity in OLS Regression Models/6. LASSO Regression.mp4	10.84 MB
3. Deal with Multicollinearity in OLS Regression Models/6. LASSO Regression.vtt	4.45 kB
3. Deal with Multicollinearity in OLS Regression Models/7. Conclusion to Section 3.mp4	5.23 MB
3. Deal with Multicollinearity in OLS Regression Models/7. Conclusion to Section 3.vtt	2.20 kB
4. Variable & Model Selection/1. Why Do Any Kind of Selection.mp4	10.55 MB
4. Variable & Model Selection/1. Why Do Any Kind of Selection.vtt	5.52 kB
4. Variable & Model Selection/2. Select the Most Suitable OLS Regression Model.mp4	33.28 MB
4. Variable & Model Selection/2. Select the Most Suitable OLS Regression Model.vtt	12.65 kB
4. Variable & Model Selection/3. Select Model Subsets.mp4	19.59 MB
4. Variable & Model Selection/3. Select Model Subsets.vtt	9.23 kB
4. Variable & Model Selection/4. Machine Learning Perspective on Evaluate Regression Model Accuracy.mp4	16.92 MB
4. Variable & Model Selection/4. Machine Learning Perspective on Evaluate Regression Model Accuracy.vtt	7.77 kB
4. Variable & Model Selection/5. Evaluate Regression Model Performance.mp4	34.85 MB
4. Variable & Model Selection/5. Evaluate Regression Model Performance.vtt	15.29 kB
4. Variable & Model Selection/6. LASSO Regression for Variable Selection.mp4	8.31 MB
4. Variable & Model Selection/6. LASSO Regression for Variable Selection.vtt	4.13 kB
4. Variable & Model Selection/7. Identify the Contribution of Predictors in Explaining the Variation in Y.mp4	21.90 MB
4. Variable & Model Selection/7. Identify the Contribution of Predictors in Explaining the Variation in Y.vtt	8.55 kB
4. Variable & Model Selection/8. Conclusions to Section 4.mp4	3.88 MB
4. Variable & Model Selection/8. Conclusions to Section 4.vtt	1.81 kB
5. Dealing With Other Violations of the OLS Regression Models/1. Data Transformations.mp4	28.99 MB
5. Dealing With Other Violations of the OLS Regression Models/1. Data Transformations.vtt	12.39 kB
5. Dealing With Other Violations of the OLS Regression Models/2. Robust Regression-Deal with Outliers.mp4	16.53 MB
5. Dealing With Other Violations of the OLS Regression Models/2. Robust Regression-Deal with Outliers.vtt	6.99 kB
5. Dealing With Other Violations of the OLS Regression Models/3. Dealing with Heteroscedasticity.mp4	18.29 MB
5. Dealing With Other Violations of the OLS Regression Models/3. Dealing with Heteroscedasticity.vtt	7.13 kB
5. Dealing With Other Violations of the OLS Regression Models/4. Conclusions to Section 5.mp4	2.90 MB
5. Dealing With Other Violations of the OLS Regression Models/4. Conclusions to Section 5.vtt	1.23 kB
6. Generalized Linear Models(GLMs)/1. What are GLMs.mp4	11.84 MB
6. Generalized Linear Models(GLMs)/1. What are GLMs.vtt	5.38 kB
6. Generalized Linear Models(GLMs)/2. Logistic regression.mp4	39.06 MB
6. Generalized Linear Models(GLMs)/2. Logistic regression.vtt	16.11 kB
6. Generalized Linear Models(GLMs)/3. Logistic Regression for Binary Response Variable.mp4	26.36 MB
6. Generalized Linear Models(GLMs)/3. Logistic Regression for Binary Response Variable.vtt	9.46 kB
6. Generalized Linear Models(GLMs)/4. Multinomial Logistic Regression.mp4	15.43 MB
6. Generalized Linear Models(GLMs)/4. Multinomial Logistic Regression.vtt	15.43 MB
6. Generalized Linear Models(GLMs)/5. Regression for Count Data.mp4	14.13 MB
6. Generalized Linear Models(GLMs)/5. Regression for Count Data.vtt	6.19 kB
6. Generalized Linear Models(GLMs)/6. Goodness of fit testing.mp4	8.88 MB
6. Generalized Linear Models(GLMs)/6. Goodness of fit testing.vtt	4.10 kB
6. Generalized Linear Models(GLMs)/7. Conclusions to Section 6.mp4	5.81 MB
6. Generalized Linear Models(GLMs)/7. Conclusions to Section 6.vtt	2.35 kB
7. Working with Non-Parametric and Non-Linear Data/1. Work With Non-Parametric and Non-Linear Data.html	669.00 B
7. Working with Non-Parametric and Non-Linear Data/10. Gradient Boosting Regression.mp4	11.15 MB
7. Working with Non-Parametric and Non-Linear Data/10. Gradient Boosting Regression.vtt	4.51 kB
7. Working with Non-Parametric and Non-Linear Data/11. ML Model Selection.mp4	12.93 MB
7. Working with Non-Parametric and Non-Linear Data/11. ML Model Selection.vtt	6.09 kB
7. Working with Non-Parametric and Non-Linear Data/12. Conclusions to Section 7.mp4	4.09 MB
7. Working with Non-Parametric and Non-Linear Data/12. Conclusions to Section 7.vtt	1.98 kB
7. Working with Non-Parametric and Non-Linear Data/2. Polynomial and Non-linear regression.mp4	24.24 MB
7. Working with Non-Parametric and Non-Linear Data/2. Polynomial and Non-linear regression.vtt	10.13 kB
7. Working with Non-Parametric and Non-Linear Data/3. Generalized Additive Models (GAMs) in R.mp4	38.52 MB
7. Working with Non-Parametric and Non-Linear Data/3. Generalized Additive Models (GAMs) in R.vtt	13.23 kB
7. Working with Non-Parametric and Non-Linear Data/3.1 gam.txt.txt	2.04 kB
7. Working with Non-Parametric and Non-Linear Data/4. Boosted GAM Regression.mp4	15.81 MB
7. Working with Non-Parametric and Non-Linear Data/4. Boosted GAM Regression.vtt	5.52 kB
7. Working with Non-Parametric and Non-Linear Data/4.1 bgam.txt.txt	1.42 kB
7. Working with Non-Parametric and Non-Linear Data/5. Multivariate Adaptive Regression Splines (MARS).mp4	21.66 MB
7. Working with Non-Parametric and Non-Linear Data/5. Multivariate Adaptive Regression Splines (MARS).vtt	8.85 kB
7. Working with Non-Parametric and Non-Linear Data/6. Machine Learning Regression-Tree Based Methods.html	468.00 B
7. Working with Non-Parametric and Non-Linear Data/7. CART-Regression Trees in R.mp4	25.17 MB
7. Working with Non-Parametric and Non-Linear Data/7. CART-Regression Trees in R.vtt	12.25 kB
7. Working with Non-Parametric and Non-Linear Data/8. Conditional Inference Trees.mp4	14.95 MB
7. Working with Non-Parametric and Non-Linear Data/8. Conditional Inference Trees.vtt	6.18 kB
7. Working with Non-Parametric and Non-Linear Data/9. Random Forest(RF).mp4	28.20 MB
7. Working with Non-Parametric and Non-Linear Data/9. Random Forest(RF).vtt	11.94 kB
Udemy Course downloader.txt	94.00 B
udemycoursedownloader.com.url	132.00 B

Description

Learn Complete Hands-On Regression Analysis for Practical Statistical Modelling and Machine Learning in R

With so many R Statistics & Machine Learning courses around, why enroll for this ?

Regression analysis is one of the central aspects of both statistical and machine learning based analysis. This course will teach you regression analysis for both statistical data analysis and machine learning in R in a practical hands-on manner. It explores the relevant concepts in a practical manner from basic to expert level. This course can help you achieve better grades, give you new analysis tools for your academic career, implement your knowledge in a work setting or make business forecasting related decisions. All of this while exploring the wisdom of an Oxford and Cambridge educated researcher.