Packt | Regression Analysis for Statistics and Machine Learning i...

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By : Minerva Singh
Released : November 28, 2019 (New Release!)
Torrent Contains : 63 Files, 9 Folders
Course Source : https://www.packtpub.com/programming/regression-analysis-for-statistics-and-machine-learning-in-r-video

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.



Files:

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1.Get Started with Practical Regression Analysis in R
  • 01.INTRODUCTION TO THE COURSE - The Key Concepts and Software Tools.mp4 (115.6 MB)
  • 02.Difference Between Statistical Analysis & Machine Learning.mp4 (72.1 MB)
  • 03.Getting Started with R and R Studio.mp4 (22.2 MB)
  • 04.Reading in Data with R.mp4 (49.8 MB)
  • 05.Data Cleaning with R.mp4 (44.8 MB)
  • 06.Some More Data Cleaning with R.mp4 (29.0 MB)
  • 07.Basic Exploratory Data Analysis in R.mp4 (55.6 MB)
  • 08.Conclusion to Section 1.mp4 (5.3 MB)
2.Ordinary Least Square Regression Modelling
  • 09.OLS Regression- Theory.mp4 (27.7 MB)
  • 10.OLS-Implementation.mp4 (25.5 MB)
  • 11.More on Result Interpretations.mp4 (18.0 MB)
  • 12.Confidence Interval-Theory.mp4 (15.0 MB)
  • 13.Calculate the Confidence Interval in R.mp4 (8.1 MB)
  • 14.Confidence Interval and OLS Regressions.mp4 (21.3 MB)
  • 15.Linear Regression without Intercept.mp4 (9.2 MB)
  • 16.Implement ANOVA on OLS Regression.mp4 (7.5 MB)
  • 17.Multiple Linear Regression.mp4 (17.2 MB)
  • 18.Multiple Linear regression with Interaction and Dummy Variables.mp4 (30.3 MB)
  • 19.Some Basic Conditions that OLS Models Have to Fulfill.mp4 (27.6 MB)
  • 20.Conclusions to Section 2.mp4 (8.0 MB)
3.Deal with Multicollinearity in OLS Regression Models
  • 21.Identify Multicollinearity.mp4 (28.7 MB)
  • 22.Doing Regression Analyses with Correlated Predictor Variables.mp4 (14.3 MB)
  • 23.Principal Component Regression in R.mp4 (29.6 MB)
  • 24.Partial Least Square Regression in R.mp4 (19.6 MB)
  • 25.Ridge Regression in R.mp4 (20.9 MB)
  • 26.LASSO Regression.mp4 (12.6 MB)
  • 27.Conclusion to Section 3.mp4 (6.1 MB)
4.Variable & Model Selection
  • 28.Why Do Any Kind of Selection.mp4 (11.6 MB)
  • 29.Select the Most Suitable OLS Regression Model.mp4 (38.8 MB)
  • 30.Select Model Subsets.mp4 (21.1 MB)
  • 31.Machine Learning Perspective on Evaluate Regression Model Accuracy.mp4 (19.4 MB)
  • 32.Evaluate Regression Model Performance.mp4 (39.7 MB)
  • 33.LASSO Regression for Variable Selection.mp4 (9.1 MB)
  • 34.Identify the Contribution of Predictors in Explaining the Variation in Y.mp4 (24.9 MB)
  • 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)
  • 37.Robust Regression-Deal with Outliers.mp4 (19.1 MB)
  • 38.Dealing with Heteroscedasticity.mp4 (14.9 MB)
  • 39.Conclusions to Section 5.mp4 (3.4 MB)
6.Generalized Linear Models (GLMs)
  • 40.What are GLMs.mp4 (12.7 MB)
  • 41.Logistic regression.mp4 (44.4 MB)
  • 42.Logistic Regression for Binary Response Variable.mp4 (31.7 MB)
  • 43.Multinomial Logistic Regression.mp4 (18.2 MB)
  • 44.Regression for Count Data.mp4 (16.1 MB)
  • 45.Goodness of fit testing.mp4 (87.2 MB)
  • 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)
  • 48.Generalized Additive Models (GAMs) in R.mp4 (39.9 MB)
  • 49.Boosted GAM Regression.mp4 (16.5 MB)
  • 50.Multivariate Adaptive Regression Splines (MARS).mp4 (26.4 MB)
  • 51.CART-Regression Trees in R.mp4 (28.3 MB)
  • 52.Conditional Inference Trees.mp4 (11.7 MB)
  • 53.Random Forest(RF).mp4 (20.5 MB)
  • 54.Gradient Boosting Regression.mp4 (8.6 MB)
  • 55.ML Model Selection.mp4 (102.2 MB)
  • 56.Conclusions to Section 7.mp4 (24.9 MB)
Exercise Files
  • code_9781838987862.zip (28.0 MB)

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