Udemy

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English

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Data Science

Category

Regression Analysis for Statistics & Machine Learning in R

Learn Complete Hands-On Regression Analysis for Practical Statistical Modelling and Machine Learning in R

4.38 (492 reviews)

Students

7.5 hours

Content

Aug 2021

Last Update
Regular Price


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


Description

            With so many R Statistics & 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 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, perusal of numerous books and publishing statistically rich papers in renowned international journal like PLOS One. Specifically the course will:

   (a) Take the students with a basic level statistical knowledge to performing 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, majority of the course will focus on implementing different  techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects. 

TAKE ACTION TODAY! I will personally support you and ensure your experience with this course is a success.


Screenshots

Regression Analysis for Statistics & Machine Learning in R
Regression Analysis for Statistics & Machine Learning in R
Regression Analysis for Statistics & Machine Learning in R
Regression Analysis for Statistics & Machine Learning in R

Content

Get Started with Practical Regression Analysis in R

INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools

Data For the Course

Difference Between Statistical Analysis & Machine Learning

Getting Started with R and R Studio

Reading in Data with R

Data Cleaning with R

Some More Data Cleaning with R

Basic Exploratory Data Analysis in R

Conclusion to Section 1

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

Deal with Multicollinearity in OLS Regression Models

Identify Multicollinearity

Doing Regression Analyses with Correlated Predictor Variables

Principal Component Regression in R

Partial Least Square Regression in R

Ridge Regression in R

LASSO Regression

Conclusion to Section 3

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

Dealing With Other Violations of the OLS Regression Models

Data Transformations

Robust Regression-Deal with Outliers

Dealing with Heteroscedasticity

Conclusions to Section 5

Generalized Linear Models(GLMs)

What are GLMs?

Logistic regression

Logistic Regression for Binary Response Variable

Multinomial Logistic Regression

Regression for Count Data

Goodness of fit testing

Conclusions to Section 6

Working with Non-Parametric and Non-Linear Data

Work With Non-Parametric and Non-Linear Data

Polynomial and Non-linear regression

Generalized Additive Models (GAMs) in R

Boosted GAM Regression

Multivariate Adaptive Regression Splines (MARS)

Machine Learning Regression-Tree Based Methods

CART-Regression Trees in R

Conditional Inference Trees

Random Forest(RF)

Gradient Boosting Regression

ML Model Selection

Conclusions to Section 7

Miscellaneous Lectures

Read in DTA Extension File


Reviews

A
Anonymized10 September 2020

The course contains very well researched lectures. The delivery of lectures has been made very interesting by the instructor. continual interest in the course makes it easy to grasp the contents.

T
Tomas30 June 2020

Good level of complexity. It includes basic concepts and the several steps and methods needed to work on regression analysis. My only comment is that some data files for practical exercises are not easily locatable

O
Oscar28 June 2020

The course is sloppy in many aspects. Resources that were apparently available, were not actually available. The instructor loaded in csv files but did not let students know where these could be accessed so that one could follow along with the same data. Strange use of terminology as well, at times. It makes one wonder whether the instructor actually knows the subject matter or is just reading from another source. The final chapters of the course were the worst. There was barely any explanation of how the regression or machine learning functions are properly implemented. It was more like "run this code, then run this, then run this,...". Ok, yes we could type the code ourselves, but it would be way more useful to know why.

J
Julio7 June 2020

Excellent theoretical and practical part, it just lacks a little more dynamism in the presentation of the slides

M
Micah2 May 2020

Excellent in-depth introduction to linear and multiple linear regression. Clear explanations provide valuable insights and context into when and why to use Regression or pursue Machine Learning techniques instead.

S
Steven25 December 2019

As an instructor, I find the speed too slow; in addition, the instructor reads the overheads - not what I expected in such a course. Finally, this course seems to be pieced together. For example, in section 3 - Difference between Statistical and Machine Learning, the instructor refers to the completion of a past session - there was no past session dealing with the issues raised!

M
Melani7 September 2019

Excellent course & the topics covered here are not available in any other course on Udemy or outside.

H
Haifeng24 June 2019

Half of the contents were little basic for me and the instructor's illustration was not as fluent, even had to correct some of the previous videos. And all the data sets used in this course were over-simplified compared to real-world examples. Hope this can be improved in the future

J
Jensi17 June 2019

I think this course is pretty good. It gave me more confidence to work with R through a lot of practice. Thank you!

R
Raja14 June 2019

I learnt more advanced techniques from the course. All the coding text in the course is clear and readable.

A
Amitaf13 June 2019

The instructor answer properly for your all requirements. Overall useful and a good start for a beginner

R
Rat12 June 2019

She covered machine learning algorithms very perfectly. I am glad I chose this course over the other available courses.

T
Ty7 June 2019

Good introduction to navigating R for someone who is new. Very superficial in terms of harnessing each algo used.

O
Oscar4 May 2019

Positives - This course is very rich in statistical material. - Most of the course goes through regression techniques and packages used and when to use them. These include caret, ggplot2, metrics and others that may be used for specific situations. - Most of the time it is easy to follow, and gives good explanations on concepts and topics for data analyses. Negatives - Sometimes explanations are not fully explained and sometimes need to be clearer. Most of the time this is down to the accent but this is a minor complaint. - The data for Section 1 is in the Q and A instead of as a link before a lecture. - It is hard sometimes to follow different methods of processing data without mathematical proof on how certain techniques work being present. Recommendations for Improvement - Show mathematical proof of concepts being taught would be very advantageous. Especially to those who are mathematically minded. - There needs to be a slide in section 1 at the start for the github website that has the data in the first section. This would stop a lot of Q and A about where the data is. - More detailed explanations and uses of the data would be great. - Having some sort of end of section or course case study would help see how all the knowledge accumulated can all come together.

S
Scott30 January 2019

This course was OK. Some repetition from other course. She sometimes got confused in her own code. I would have liked more explanation of the arguments in the different models. That would help in understanding the utilization.


1338502

Udemy ID

9/2/2017

Course created date

11/20/2019

Course Indexed date
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