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

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 7.5 Hours | 1.07 GB

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

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.

What you’ll 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

**Table of Contents**

**Get Started with Practical Regression Analysis in R**

1 INTRODUCTION TO THE COURSE The Key Concepts and Software Tools

2 Data For the Course

3 Difference Between Statistical Analysis & Machine Learning

4 Getting Started with R and R Studio

5 Reading in Data with R

6 Data Cleaning with R

7 Some More Data Cleaning with R

8 Basic Exploratory Data Analysis in R

9 Conclusion to Section 1

**Ordinary Least Square Regression Modelling**

10 OLS Regression- Theory

11 Multiple Linear regression with Interaction and Dummy Variables

12 Some Basic Conditions that OLS Models Have to Fulfill

13 Conclusions to Section 2

14 OLS-Implementation

15 More on Result Interpretations

16 Confidence Interval-Theory

17 Calculate the Confidence Interval in R

18 Confidence Interval and OLS Regressions

19 Linear Regression without Intercept

20 Implement ANOVA on OLS Regression

21 Multiple Linear Regression

**Deal with Multicollinearity in OLS Regression Models**

22 Identify Multicollinearity

23 Doing Regression Analyses with Correlated Predictor Variables

24 Principal Component Regression in R

25 Partial Least Square Regression in R

26 Ridge Regression in R

27 LASSO Regression

28 Conclusion to Section 3

**Variable & Model Selection**

29 Why Do Any Kind of Selection

30 Select the Most Suitable OLS Regression Model

31 Select Model Subsets

32 Machine Learning Perspective on Evaluate Regression Model Accuracy

33 Evaluate Regression Model Performance

34 LASSO Regression for Variable Selection

35 Identify the Contribution of Predictors in Explaining the Variation in Y

36 Conclusions to Section 4

**Dealing With Other Violations of the OLS Regression Models**

37 Data Transformations

38 Robust Regression-Deal with Outliers

39 Dealing with Heteroscedasticity

40 Conclusions to Section 5

**Generalized Linear Models(GLMs)**

41 What are GLMs

42 Logistic regression

43 Logistic Regression for Binary Response Variable

44 Multinomial Logistic Regression

45 Regression for Count Data

46 Goodness of fit testing

47 Conclusions to Section 6

**Working with Non-Parametric and Non-Linear Data**

48 Work With Non-Parametric and Non-Linear Data

49 Gradient Boosting Regression

50 ML Model Selection

51 Conclusions to Section 7

52 Polynomial and Non-linear regression

53 Generalized Additive Models (GAMs) in R

54 Boosted GAM Regression

55 Multivariate Adaptive Regression Splines (MARS)

56 Machine Learning Regression-Tree Based Methods

57 CART-Regression Trees in R

58 Conditional Inference Trees

59 Random Forest(RF)

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