Description

Learn complete hands-on Regression analysis for practical Statistical modelling and Machine Learning in R

With so many R Statistics and Machine Learning courses around, why enroll for this? Regression analysis is one of the central aspects of both Statistics and Machine Learning based analysis. This course will teach you Regression analysis for both Statistical data analysis and ML in R. It explores relevant concepts in a practical way, from basic to expert level. This course can help you achieve better grades, gain new analysis tools for your academic career, implement your knowledge in a work setting, and make business forecasting-related decisions. You will go all the way from implementing and inferring simple OLS (Ordinary Least Square) regression models to dealing with issues of multicollinearity in regression to ML based regression models. Become a Regression analysis expert and harness the power of R for your analysis • Get started with R and RStudio. Install these on your system, learn to load packages, and read in different types of data in R
• Carry out data cleaning and data visualization using R
• Implement Ordinary Least Square (OLS) regression in R and learn how to interpret the results.
• Learn how to deal with multicollinearity both through the variable selection and regularization techniques such as ridge regression
• Carry out variable and regression model selection using both statistical and machine learning techniques, including using cross-validation methods.
• Evaluate the regression model accuracy
• Implement Generalized Linear Models (GLMs) such as logistic regression and Poisson regression. Use logistic regression as a binary classifier to distinguish between male and female voices.
• Use non-parametric techniques such as Generalized Additive Models (GAMs) to work with non-linear and non-parametric data.
• Work with tree-based ML models All the code and supporting files for this course are available at - https://github.com/PacktPublishing/Regression-Analysis-for-Statistics-and-Machine-Learning-in-R

What You Will Learn

Implement and infer Ordinary Least Square (OLS) regression using R
Apply statistical and ML based regression models to deal with problems such as multicollinearity
Carry out the variable selection and assess model accuracy using techniques such as cross-validation
Implement and infer Generalized Linear Models (GLMs), including using logistic regression as a binary classifier

Audience

This course is for students interested in getting started with data science applications in the RStudio environment. Students wishing to learn to implement statistical modeling techniques for regression analysis on real data. Anyone with prior exposure to R who wants to get started with practical data science.

Approach

This is a practical, hands-on course. We will spend some time dealing with some theoretical concepts related to both statistical and machine learning regression analysis. However, the majority of the course will focus on implementing different techniques on real data and interpreting the results. After each video, you will learn a new concept or technique which you can apply to your own projects.

Key Features

Provided practical indepth training for you to get started with R * Perform some of the most common advanced regression analysis-based techniques * Use R to perform different statistical and machine learning data analysis and visualization tasks

Github Repo

https://github.com/packtpublishing/regression-analysis-for-statistics-and-machine-learning-in-r

About the Author

Minerva Singh

Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a part-time Data Scientist. As part of her research, she must carry out extensive data analysis, including spatial data analysis. For this purpose, she prefers to use a combination of freeware tools: R, QGIS, and Python. She does most of her spatial data analysis work using R and QGIS. Apart from being free, these are very powerful tools for data visualization, processing, and analysis. She also holds an MPhil degree in Geography and Environment from Oxford University. She has honed her statistical and data analysis skills through several MOOCs, including The Analytics Edge and Statistical. In addition to spatial data analysis, she is also proficient in statistical analysis, machine learning, and data mining.

Course Outline

1. Get Started with Practical Regression Analysis in R

1. INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools

Get Started with Practical Regression Analysis in R: INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools

2. Difference Between Statistical Analysis & Machine Learning

Get Started with Practical Regression Analysis in R: Difference Between Statistical Analysis & Machine Learning

3. Getting Started with R and R Studio

Get Started with Practical Regression Analysis in R: Getting Started with R and R Studio

4. Reading in Data with R

Get Started with Practical Regression Analysis in R: Reading in Data with R

5. Data Cleaning with R

Get Started with Practical Regression Analysis in R: Data Cleaning with R

6. Some More Data Cleaning with R

Get Started with Practical Regression Analysis in R: Some More Data Cleaning with R

7. Basic Exploratory Data Analysis in R

Get Started with Practical Regression Analysis in R: Basic Exploratory Data Analysis in R

8. Conclusion to Section 1

Get Started with Practical Regression Analysis in R: Conclusion to Section 1

2. Ordinary Least Square Regression Modelling

1. OLS Regression- Theory

Ordinary Least Square Regression Modelling: OLS Regression- Theory

2. OLS-Implementation

Ordinary Least Square Regression Modelling: OLS-Implementation

3. More on Result Interpretations

Ordinary Least Square Regression Modelling: More on Result Interpretations

4. Confidence Interval-Theory

Ordinary Least Square Regression Modelling: Confidence Interval-Theory

5. Calculate the Confidence Interval in R

Ordinary Least Square Regression Modelling: Calculate the Confidence Interval in R

6. Confidence Interval and OLS Regressions

Ordinary Least Square Regression Modelling: Confidence Interval and OLS Regressions

7. Linear Regression without Intercept

Ordinary Least Square Regression Modelling: Linear Regression without Intercept

8. Implement ANOVA on OLS Regression

Ordinary Least Square Regression Modelling: Implement ANOVA on OLS Regression

9. Multiple Linear Regression

Ordinary Least Square Regression Modelling: Multiple Linear Regression

10. Multiple Linear regression with Interaction and Dummy Variables

Ordinary Least Square Regression Modelling: Multiple Linear regression with Interaction and Dummy Variables

11. Some Basic Conditions that OLS Models Have to Fulfill

Ordinary Least Square Regression Modelling: Some Basic Conditions that OLS Models Have to Fulfill

12. Conclusions to Section 2

Ordinary Least Square Regression Modelling: Conclusions to Section 2

3. Deal with Multicollinearity in OLS Regression Models

1. Identify Multicollinearity

Deal with Multicollinearity in OLS Regression Models: Identify Multicollinearity

2. Doing Regression Analyses with Correlated Predictor Variables

Deal with Multicollinearity in OLS Regression Models: Doing Regression Analyses with Correlated Predictor Variables

3. Principal Component Regression in R

Deal with Multicollinearity in OLS Regression Models: Principal Component Regression in R

4. Partial Least Square Regression in R

Deal with Multicollinearity in OLS Regression Models: Partial Least Square Regression in R

5. Ridge Regression in R

Deal with Multicollinearity in OLS Regression Models: Ridge Regression in R

6. LASSO Regression

Deal with Multicollinearity in OLS Regression Models: LASSO Regression

7. Conclusion to Section 3

Deal with Multicollinearity in OLS Regression Models: Conclusion to Section 3

4. Variable & Model Selection

1. Why Do Any Kind of Selection?

Variable & Model Selection: Why Do Any Kind of Selection?

2. Select the Most Suitable OLS Regression Model

Variable & Model Selection: Select the Most Suitable OLS Regression Model

3. Select Model Subsets

Variable & Model Selection: Select Model Subsets

4. Machine Learning Perspective on Evaluate Regression Model Accuracy

Variable & Model Selection: Machine Learning Perspective on Evaluate Regression Model Accuracy

5. Evaluate Regression Model Performance

Variable & Model Selection: Evaluate Regression Model Performance

6. LASSO Regression for Variable Selection

Variable & Model Selection: LASSO Regression for Variable Selection

7. Identify the Contribution of Predictors in Explaining the Variation in Y

Variable & Model Selection: Identify the Contribution of Predictors in Explaining the Variation in Y

8. Conclusions to Section 4

Variable & Model Selection: Conclusions to Section 4

5. Dealing with Other Violations of the OLS Regression Models

1. Data Transformations

Dealing with Other Violations of the OLS Regression Models: Data Transformations

2. Robust Regression-Deal with Outliers

Dealing with Other Violations of the OLS Regression Models: Robust Regression-Deal with Outliers

3. Dealing with Heteroscedasticity

Dealing with Other Violations of the OLS Regression Models: Dealing with Heteroscedasticity

4. Conclusions to Section 5

Dealing with Other Violations of the OLS Regression Models: Conclusions to Section 5

6. Generalized Linear Models (GLMs)

1. What are GLMs?

Generalized Linear Models (GLMs): What are GLMs?

2. Logistic regression

Generalized Linear Models (GLMs): Logistic regression

3. Logistic Regression for Binary Response Variable

Generalized Linear Models (GLMs): Logistic Regression for Binary Response Variable

4. Multinomial Logistic Regression

Generalized Linear Models (GLMs): Multinomial Logistic Regression

5. Regression for Count Data

Generalized Linear Models (GLMs): Regression for Count Data

6. Goodness of fit testing

Generalized Linear Models (GLMs): Goodness of fit testing

7. Conclusions to Section 6

Generalized Linear Models (GLMs): Conclusions to Section 6

7. Working with Non-Parametric and Non-Linear Data

1. Polynomial and Non-linear regression

Working with Non-Parametric and Non-Linear Data: Polynomial and Non-linear regression

2. Generalized Additive Models (GAMs) in R

Working with Non-Parametric and Non-Linear Data: Generalized Additive Models (GAMs) in R

3. Boosted GAM Regression

Working with Non-Parametric and Non-Linear Data: Boosted GAM Regression

4. Multivariate Adaptive Regression Splines (MARS)

Working with Non-Parametric and Non-Linear Data: Multivariate Adaptive Regression Splines (MARS)

5. CART-Regression Trees in R

Working with Non-Parametric and Non-Linear Data: CART-Regression Trees in R

6. Conditional Inference Trees

Working with Non-Parametric and Non-Linear Data: Conditional Inference Trees

7. Random Forest(RF)

Working with Non-Parametric and Non-Linear Data: Random Forest(RF)

8. Gradient Boosting Regression

Working with Non-Parametric and Non-Linear Data: Gradient Boosting Regression

9. ML Model Selection

Working with Non-Parametric and Non-Linear Data: ML Model Selection

10. Conclusions to Section 7

Working with Non-Parametric and Non-Linear Data: Conclusions to Section 7

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Regression Analysis for Statistics & Machine Learning in R

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