Regression Analysis for Statistics & Machine Learning in R

Regression Analysis for Statistics & Machine Learning in R, available at $84.99, has an average rating of 4.55, with 64 lectures, based on 547 reviews, and has 4650 subscribers.

You will learn about Implement and infer Ordinary Least Square (OLS) regression using R Apply statistical and machine learning based regression models to deals with problems such as multicollinearity Carry out variable selection and assess model accuracy using techniques like cross-validation Implement and infer Generalized Linear Models (GLMS), including using logistic regression as a binary classifier Build machine learning based regression models and test their robustness in R Learn when and how machine learning models should be applied Compare different different machine learning algorithms for regression modelling This course is ideal for individuals who are People who have completed my course on Statistical Modeling for Data Analysis in R (or equivalent experience) or People with basic knowledge of R based statistical modelling or People with knowledge of linear regression modelling or People wanting to extend their knowledge of regression modelling for solving real world problems. or People wanting to learn how to apply machine learning based regression models using R or Undergraduates and postgraduates seeking to deepen their knowledge of statistical and machine learning analysis or Academic researchers seeking to learn new techniques for data analysis or Business data analysts who wish to use regression modelling for predictive analysis It is particularly useful for People who have completed my course on Statistical Modeling for Data Analysis in R (or equivalent experience) or People with basic knowledge of R based statistical modelling or People with knowledge of linear regression modelling or People wanting to extend their knowledge of regression modelling for solving real world problems. or People wanting to learn how to apply machine learning based regression models using R or Undergraduates and postgraduates seeking to deepen their knowledge of statistical and machine learning analysis or Academic researchers seeking to learn new techniques for data analysis or Business data analysts who wish to use regression modelling for predictive analysis.

Enroll now: Regression Analysis for Statistics & Machine Learning in R

Summary

Title: Regression Analysis for Statistics & Machine Learning in R

Price: $84.99

Average Rating: 4.55

Number of Lectures: 64

Number of Published Lectures: 64

Number of Curriculum Items: 64

Number of Published Curriculum Objects: 64

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Implement and infer Ordinary Least Square (OLS) regression using R
Apply statistical and machine learning based regression models to deals with problems such as multicollinearity
Carry out variable selection and assess model accuracy using techniques like cross-validation
Implement and infer Generalized Linear Models (GLMS), including using logistic regression as a binary classifier
Build machine learning based regression models and test their robustness in R
Learn when and how machine learning models should be applied
Compare different different machine learning algorithms for regression modelling

Who Should Attend

People who have completed my course on Statistical Modeling for Data Analysis in R (or equivalent experience)
People with basic knowledge of R based statistical modelling
People with knowledge of linear regression modelling
People wanting to extend their knowledge of regression modelling for solving real world problems.
People wanting to learn how to apply machine learning based regression models using R
Undergraduates and postgraduates seeking to deepen their knowledge of statistical and machine learning analysis
Academic researchers seeking to learn new techniques for data analysis
Business data analysts who wish to use regression modelling for predictive analysis

Target Audiences

People who have completed my course on Statistical Modeling for Data Analysis in R (or equivalent experience)
People with basic knowledge of R based statistical modelling
People with knowledge of linear regression modelling
People wanting to extend their knowledge of regression modelling for solving real world problems.
People wanting to learn how to apply machine learning based regression models using R
Undergraduates and postgraduates seeking to deepen their knowledge of statistical and machine learning analysis
Academic researchers seeking to learn new techniques for data analysis
Business data analysts who wish to use regression modelling for predictive analysis

With so many R Statistics & Machine Learning courses around, why enrol 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 manner. It explores the relevant concepts in a practical manner from basic to expert level. This course can help you achieve better grades, give you new analysis tools for your academic career, implement your knowledge in a work setting or make business forecasting related decisions. All of this while exploring the wisdom of an Oxford and Cambridge educated researcher.

My name is MINERVA SINGH and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. This course is based on my years of regression modelling experience and implementing different regression models on real life data. Most statistics and machine learning courses and books only touch upon the basic aspects of regression analysis. This does not teach the students about all the different regression analysis techniques they can apply to their own data in both academic and business setting, resulting in inaccurate modelling. My course will change this. 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 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 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
Implement machine learning methods such as random forest regression and gradient boosting machine regression for improved regression prediction accuracy.
Carry out model selection

Become a Regression Analysis Pro and Apply Your Knowledge on Real-Life Data

This course is your one shot way of acquiring the knowledge of statistical and machine learning analysis that I acquired from the rigorous training received at two of the best universities in the world, the perusal of numerous books and publishing statistically rich papers in a renowned international journal like PLOS One. Specifically, the course will:

(a) Take the students with a basic level of statistical knowledge to perform some of the most common advanced regression analysis based techniques

(b) Equip students to use R for performing the different statistical and machine learning data analysis and visualization tasks

(c) Introduce some of the most important statistical and machine learning concepts to students in a practical manner such that the students can apply these concepts for practical data analysis and interpretation

(d) Students will get a strong background in some of the most important statistical and machine learning concepts for regression analysis.

(e) Students will be able to decide which regression analysis techniques are best suited to answer their research questions and applicable to their data and interpret the results

It is a practical, hands-on course, i.e. we will spend some time dealing with some of the 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 may apply to your own projects.

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Course Curriculum

Chapter 1: Get Started with Practical Regression Analysis in R

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

Lecture 2: Data For the Course

Lecture 3: Difference Between Statistical Analysis & Machine Learning

Lecture 4: Getting Started with R and R Studio

Lecture 5: Reading in Data with R

Lecture 6: Data Cleaning with R

Lecture 7: Some More Data Cleaning with R

Lecture 8: Basic Exploratory Data Analysis in R

Lecture 9: Conclusion to Section 1

Chapter 2: Ordinary Least Square Regression Modelling

Lecture 1: OLS Regression- Theory

Lecture 2: OLS-Implementation

Lecture 3: More on Result Interpretations

Lecture 4: Confidence Interval-Theory

Lecture 5: Calculate the Confidence Interval in R

Lecture 6: Confidence Interval and OLS Regressions

Lecture 7: Linear Regression without Intercept

Lecture 8: Implement ANOVA on OLS Regression

Lecture 9: Multiple Linear Regression

Lecture 10: Multiple Linear regression with Interaction and Dummy Variables

Lecture 11: Some Basic Conditions that OLS Models Have to Fulfill

Lecture 12: Conclusions to Section 2

Chapter 3: Deal with Multicollinearity in OLS Regression Models

Lecture 1: Identify Multicollinearity

Lecture 2: Doing Regression Analyses with Correlated Predictor Variables

Lecture 3: Principal Component Regression in R

Lecture 4: Partial Least Square Regression in R

Lecture 5: Ridge Regression in R

Lecture 6: LASSO Regression

Lecture 7: Conclusion to Section 3

Chapter 4: Variable & Model Selection

Lecture 1: Why Do Any Kind of Selection?

Lecture 2: Select the Most Suitable OLS Regression Model

Lecture 3: Select Model Subsets

Lecture 4: Machine Learning Perspective on Evaluate Regression Model Accuracy

Lecture 5: Evaluate Regression Model Performance

Lecture 6: LASSO Regression for Variable Selection

Lecture 7: Identify the Contribution of Predictors in Explaining the Variation in Y

Lecture 8: Conclusions to Section 4

Chapter 5: Dealing With Other Violations of the OLS Regression Models

Lecture 1: Data Transformations

Lecture 2: Robust Regression-Deal with Outliers

Lecture 3: Dealing with Heteroscedasticity

Lecture 4: Conclusions to Section 5

Chapter 6: Generalized Linear Models(GLMs)

Lecture 1: What are GLMs?

Lecture 2: Logistic regression

Lecture 3: Logistic Regression for Binary Response Variable

Lecture 4: Multinomial Logistic Regression

Lecture 5: Regression for Count Data

Lecture 6: Goodness of fit testing

Lecture 7: Conclusions to Section 6

Chapter 7: Working with Non-Parametric and Non-Linear Data

Lecture 1: Work With Non-Parametric and Non-Linear Data

Lecture 2: Polynomial and Non-linear regression

Lecture 3: Generalized Additive Models (GAMs) in R

Lecture 4: Boosted GAM Regression

Lecture 5: Multivariate Adaptive Regression Splines (MARS)

Lecture 6: Machine Learning Regression-Tree Based Methods

Lecture 7: CART-Regression Trees in R

Lecture 8: Conditional Inference Trees

Lecture 9: Random Forest(RF)

Lecture 10: Gradient Boosting Regression

Lecture 11: ML Model Selection

Lecture 12: Conclusions to Section 7

Chapter 8: Miscellaneous Lectures

Lecture 1: Read in DTA Extension File

Lecture 2: Getting Acquainted with Github Desktop

Lecture 3: Using R Colab

Lecture 4: Group By Time

Lecture 5: POSIT

Instructors

Minerva Singh
Bestselling Instructor & Data Scientist(Cambridge Uni)

Rating Distribution

1 stars: 19 votes
2 stars: 18 votes
3 stars: 80 votes
4 stars: 132 votes
5 stars: 298 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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