Regression Analysis for Statistics and Machine Learning in R [Video]
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Free ChapterGet Started with Practical Regression Analysis in R
-
Ordinary Least Square Regression Modelling
- OLS Regression- Theory
- OLS-Implementation
- More on Result Interpretations
- Confidence Interval-Theory
- Calculate the Confidence Interval in R
- Confidence Interval and OLS Regressions
- Linear Regression without Intercept
- Implement ANOVA on OLS Regression
- Multiple Linear Regression
- Multiple Linear regression with Interaction and Dummy Variables
- Some Basic Conditions that OLS Models Have to Fulfill
- Conclusions to Section 2
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Deal with Multicollinearity in OLS Regression Models
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Variable & Model Selection
- Why Do Any Kind of Selection?
- Select the Most Suitable OLS Regression Model
- Select Model Subsets
- Machine Learning Perspective on Evaluate Regression Model Accuracy
- Evaluate Regression Model Performance
- LASSO Regression for Variable Selection
- Identify the Contribution of Predictors in Explaining the Variation in Y
- Conclusions to Section 4
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Dealing with Other Violations of the OLS Regression Models
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Generalized Linear Models (GLMs)
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Working with Non-Parametric and Non-Linear Data
About this
video
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
- Publication date:
- November 2019
- Publisher
- Packt
- Duration
- 7 hours 18 minutes
- ISBN
- 9781838987862