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[FreeCoursesOnline.Me] [Packt] Regression Analysis For Statistics And Machine Learning In R [FCO]
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Learn complete hands-on Regression Analysis for practical Statistical Modelling and Machine Learning in R
Video Details
ISBN 9781838987862
Course Length 7 hours 18 minutes
Table of Contents
• Get Started with Practical Regression Analysis in R
• Ordinary Least Square Regression Modelling
• Deal with Multicollinearity in OLS Regression Models
• Variable & Model Selection
• Dealing with Other Violations of the OLS Regression Models
• Generalized Linear Models (GLMs)
• Working with Non-Parametric and Non-Linear Data
Learn
• Implement and infer Ordinary Least Square (OLS) regression using R
• Apply statistical- and machine-learning 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
About
With so many R Statistics and 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 way. 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 machine learning-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 machine learning 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
Features:
• Provides in-depth training in everything you need to know to get started with practical R data science
• The course will teach the student with a basic-level statistical knowledge to perform some of the most common advanced regression analysis-based techniques
• Equip students to use R to perform different statistical and machine learning data analysis and visualization tasks
Author
Minerva Singh
The author’s name is Minerva Singh. She is an Oxford University MPhil (Geography and Environment), graduate. She recently finished her Ph.D. at Cambridge University (Tropical Ecology and Conservation). She has several years of experience in analyzing real-life data from different sources in ArcGIS Desktop. She has also published her work in many international peer-reviewed journals. In addition to spatial data analysis, she is proficient in statistical analysis, machine learning and data mining. She also enjoys general programming, data visualization and web development. In addition to being a scientist and number cruncher, she is an avid traveler.
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FILE LIST
Filename
Size
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208 B
1.Get Started with Practical Regression Analysis in R/01.INTRODUCTION TO THE COURSE - The Key Concepts and Software Tools.mp4
115.6 MB
1.Get Started with Practical Regression Analysis in R/02.Difference Between Statistical Analysis & Machine Learning.mp4
72.1 MB
1.Get Started with Practical Regression Analysis in R/03.Getting Started with R and R Studio.mp4
22.2 MB
1.Get Started with Practical Regression Analysis in R/04.Reading in Data with R.mp4
49.8 MB
1.Get Started with Practical Regression Analysis in R/05.Data Cleaning with R.mp4
44.8 MB
1.Get Started with Practical Regression Analysis in R/06.Some More Data Cleaning with R.mp4
29 MB
1.Get Started with Practical Regression Analysis in R/07.Basic Exploratory Data Analysis in R.mp4
55.6 MB
1.Get Started with Practical Regression Analysis in R/08.Conclusion to Section 1.mp4
5.3 MB
2.Ordinary Least Square Regression Modelling/09.OLS Regression- Theory.mp4
27.7 MB
2.Ordinary Least Square Regression Modelling/10.OLS-Implementation.mp4
25.5 MB
2.Ordinary Least Square Regression Modelling/11.More on Result Interpretations.mp4
18 MB
2.Ordinary Least Square Regression Modelling/12.Confidence Interval-Theory.mp4
15 MB
2.Ordinary Least Square Regression Modelling/13.Calculate the Confidence Interval in R.mp4
8.1 MB
2.Ordinary Least Square Regression Modelling/14.Confidence Interval and OLS Regressions.mp4
21.3 MB
2.Ordinary Least Square Regression Modelling/15.Linear Regression without Intercept.mp4
9.2 MB
2.Ordinary Least Square Regression Modelling/16.Implement ANOVA on OLS Regression.mp4
7.5 MB
2.Ordinary Least Square Regression Modelling/17.Multiple Linear Regression.mp4
17.2 MB
2.Ordinary Least Square Regression Modelling/18.Multiple Linear regression with Interaction and Dummy Variables.mp4
30.3 MB
2.Ordinary Least Square Regression Modelling/19.Some Basic Conditions that OLS Models Have to Fulfill.mp4
27.6 MB
2.Ordinary Least Square Regression Modelling/20.Conclusions to Section 2.mp4
8 MB
3.Deal with Multicollinearity in OLS Regression Models/21.Identify Multicollinearity.mp4
28.7 MB
3.Deal with Multicollinearity in OLS Regression Models/22.Doing Regression Analyses with Correlated Predictor Variables.mp4
14.3 MB
3.Deal with Multicollinearity in OLS Regression Models/23.Principal Component Regression in R.mp4
29.6 MB
3.Deal with Multicollinearity in OLS Regression Models/24.Partial Least Square Regression in R.mp4
19.6 MB
3.Deal with Multicollinearity in OLS Regression Models/25.Ridge Regression in R.mp4
20.9 MB
3.Deal with Multicollinearity in OLS Regression Models/26.LASSO Regression.mp4
12.6 MB
3.Deal with Multicollinearity in OLS Regression Models/27.Conclusion to Section 3.mp4
6.1 MB
4.Variable & Model Selection/28.Why Do Any Kind of Selection.mp4
11.6 MB
4.Variable & Model Selection/29.Select the Most Suitable OLS Regression Model.mp4
38.8 MB
4.Variable & Model Selection/30.Select Model Subsets.mp4
21.1 MB
4.Variable & Model Selection/31.Machine Learning Perspective on Evaluate Regression Model Accuracy.mp4
19.4 MB
4.Variable & Model Selection/32.Evaluate Regression Model Performance.mp4
39.7 MB
4.Variable & Model Selection/33.LASSO Regression for Variable Selection.mp4
9.1 MB
4.Variable & Model Selection/34.Identify the Contribution of Predictors in Explaining the Variation in Y.mp4
24.9 MB
4.Variable & Model Selection/35.Conclusions to Section 4.mp4
4.5 MB
5.Dealing with Other Violations of the OLS Regression Models/36.Data Transformations.mp4
23.1 MB
5.Dealing with Other Violations of the OLS Regression Models/37.Robust Regression-Deal with Outliers.mp4
19.1 MB
5.Dealing with Other Violations of the OLS Regression Models/38.Dealing with Heteroscedasticity.mp4
14.9 MB
5.Dealing with Other Violations of the OLS Regression Models/39.Conclusions to Section 5.mp4
3.4 MB
6.Generalized Linear Models (GLMs)/40.What are GLMs.mp4
12.7 MB
6.Generalized Linear Models (GLMs)/41.Logistic regression.mp4
44.4 MB
6.Generalized Linear Models (GLMs)/42.Logistic Regression for Binary Response Variable.mp4
31.7 MB
6.Generalized Linear Models (GLMs)/43.Multinomial Logistic Regression.mp4
18.2 MB
6.Generalized Linear Models (GLMs)/44.Regression for Count Data.mp4
16.1 MB
6.Generalized Linear Models (GLMs)/45.Goodness of fit testing.mp4
87.2 MB
6.Generalized Linear Models (GLMs)/46.Conclusions to Section 6.mp4
6.7 MB
7.Working with Non-Parametric and Non-Linear Data/47.Polynomial and Non-linear regression.mp4
18.9 MB
7.Working with Non-Parametric and Non-Linear Data/48.Generalized Additive Models (GAMs) in R.mp4
39.9 MB
7.Working with Non-Parametric and Non-Linear Data/49.Boosted GAM Regression.mp4
16.5 MB
7.Working with Non-Parametric and Non-Linear Data/50.Multivariate Adaptive Regression Splines (MARS).mp4
26.4 MB
7.Working with Non-Parametric and Non-Linear Data/51.CART-Regression Trees in R.mp4
28.3 MB
7.Working with Non-Parametric and Non-Linear Data/52.Conditional Inference Trees.mp4
11.7 MB
7.Working with Non-Parametric and Non-Linear Data/53.Random Forest(RF).mp4
20.5 MB
7.Working with Non-Parametric and Non-Linear Data/54.Gradient Boosting Regression.mp4
8.6 MB
7.Working with Non-Parametric and Non-Linear Data/55.ML Model Selection.mp4
102.2 MB
7.Working with Non-Parametric and Non-Linear Data/56.Conclusions to Section 7.mp4
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