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Download Torrent "Packt | Regression Analysis for Statistics and Machine Learning in R [FCO]"

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Title:Packt | Regression Analysis for Statistics and Machine Learning in R [FCO]
Description:
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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.




Category:Tutorials > Tutorials
Lang:English  English
Total Size:1.48 GB
Info Hash:46ccad14c58fc19493e0b01ec226202b0d9cc1c0
Added By:Prom3th3uS
Date Added:31-12-2019 15:55:35

  

Files

File List: 
 File Size
0. Websites you may like/0. (1Hack.Us) Premium Tutorials-Guides-Articles & Community based Forum.url377.00 B
0. Websites you may like/2. (FreeCoursesOnline.Me) Download Udacity, Masterclass, Lynda, PHLearn, Pluralsight Free.url286.00 B
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1.Get Started with Practical Regression Analysis in R/01.INTRODUCTION TO THE COURSE - The Key Concepts and Software Tools.mp4116 MB
1.Get Started with Practical Regression Analysis in R/02.Difference Between Statistical Analysis & Machine Learning.mp472 MB
1.Get Started with Practical Regression Analysis in R/03.Getting Started with R and R Studio.mp422 MB
1.Get Started with Practical Regression Analysis in R/04.Reading in Data with R.mp450 MB
1.Get Started with Practical Regression Analysis in R/05.Data Cleaning with R.mp445 MB
1.Get Started with Practical Regression Analysis in R/06.Some More Data Cleaning with R.mp429 MB
1.Get Started with Practical Regression Analysis in R/07.Basic Exploratory Data Analysis in R.mp456 MB
1.Get Started with Practical Regression Analysis in R/08.Conclusion to Section 1.mp45 MB
2.Ordinary Least Square Regression Modelling/09.OLS Regression- Theory.mp428 MB
2.Ordinary Least Square Regression Modelling/10.OLS-Implementation.mp426 MB
2.Ordinary Least Square Regression Modelling/11.More on Result Interpretations.mp418 MB
2.Ordinary Least Square Regression Modelling/12.Confidence Interval-Theory.mp415 MB
2.Ordinary Least Square Regression Modelling/13.Calculate the Confidence Interval in R.mp48 MB
2.Ordinary Least Square Regression Modelling/14.Confidence Interval and OLS Regressions.mp421 MB
2.Ordinary Least Square Regression Modelling/15.Linear Regression without Intercept.mp49 MB
2.Ordinary Least Square Regression Modelling/16.Implement ANOVA on OLS Regression.mp47 MB
2.Ordinary Least Square Regression Modelling/17.Multiple Linear Regression.mp417 MB
2.Ordinary Least Square Regression Modelling/18.Multiple Linear regression with Interaction and Dummy Variables.mp430 MB
2.Ordinary Least Square Regression Modelling/19.Some Basic Conditions that OLS Models Have to Fulfill.mp428 MB
2.Ordinary Least Square Regression Modelling/20.Conclusions to Section 2.mp48 MB
3.Deal with Multicollinearity in OLS Regression Models/21.Identify Multicollinearity.mp429 MB
3.Deal with Multicollinearity in OLS Regression Models/22.Doing Regression Analyses with Correlated Predictor Variables.mp414 MB
3.Deal with Multicollinearity in OLS Regression Models/23.Principal Component Regression in R.mp430 MB
3.Deal with Multicollinearity in OLS Regression Models/24.Partial Least Square Regression in R.mp420 MB
3.Deal with Multicollinearity in OLS Regression Models/25.Ridge Regression in R.mp421 MB
3.Deal with Multicollinearity in OLS Regression Models/26.LASSO Regression.mp413 MB
3.Deal with Multicollinearity in OLS Regression Models/27.Conclusion to Section 3.mp46 MB
4.Variable & Model Selection/28.Why Do Any Kind of Selection.mp412 MB
4.Variable & Model Selection/29.Select the Most Suitable OLS Regression Model.mp439 MB
4.Variable & Model Selection/30.Select Model Subsets.mp421 MB
4.Variable & Model Selection/31.Machine Learning Perspective on Evaluate Regression Model Accuracy.mp419 MB
4.Variable & Model Selection/32.Evaluate Regression Model Performance.mp440 MB
4.Variable & Model Selection/33.LASSO Regression for Variable Selection.mp49 MB
4.Variable & Model Selection/34.Identify the Contribution of Predictors in Explaining the Variation in Y.mp425 MB
4.Variable & Model Selection/35.Conclusions to Section 4.mp44 MB
5.Dealing with Other Violations of the OLS Regression Models/36.Data Transformations.mp423 MB
5.Dealing with Other Violations of the OLS Regression Models/37.Robust Regression-Deal with Outliers.mp419 MB
5.Dealing with Other Violations of the OLS Regression Models/38.Dealing with Heteroscedasticity.mp415 MB
5.Dealing with Other Violations of the OLS Regression Models/39.Conclusions to Section 5.mp43 MB
6.Generalized Linear Models (GLMs)/40.What are GLMs.mp413 MB
6.Generalized Linear Models (GLMs)/41.Logistic regression.mp444 MB
6.Generalized Linear Models (GLMs)/42.Logistic Regression for Binary Response Variable.mp432 MB
6.Generalized Linear Models (GLMs)/43.Multinomial Logistic Regression.mp418 MB
6.Generalized Linear Models (GLMs)/44.Regression for Count Data.mp416 MB
6.Generalized Linear Models (GLMs)/45.Goodness of fit testing.mp487 MB
6.Generalized Linear Models (GLMs)/46.Conclusions to Section 6.mp47 MB
7.Working with Non-Parametric and Non-Linear Data/47.Polynomial and Non-linear regression.mp419 MB
7.Working with Non-Parametric and Non-Linear Data/48.Generalized Additive Models (GAMs) in R.mp440 MB
7.Working with Non-Parametric and Non-Linear Data/49.Boosted GAM Regression.mp416 MB
7.Working with Non-Parametric and Non-Linear Data/50.Multivariate Adaptive Regression Splines (MARS).mp426 MB
7.Working with Non-Parametric and Non-Linear Data/51.CART-Regression Trees in R.mp428 MB
7.Working with Non-Parametric and Non-Linear Data/52.Conditional Inference Trees.mp412 MB
7.Working with Non-Parametric and Non-Linear Data/53.Random Forest(RF).mp420 MB
7.Working with Non-Parametric and Non-Linear Data/54.Gradient Boosting Regression.mp49 MB
7.Working with Non-Parametric and Non-Linear Data/55.ML Model Selection.mp4102 MB
7.Working with Non-Parametric and Non-Linear Data/56.Conclusions to Section 7.mp425 MB
Exercise Files/code_9781838987862.zip28 MB



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