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Get up to speed with linear regression analysis for predictive machine learning and econometrics
Are you looking for a complete linear regression course that teaches you everything you need to create a linear regression model in R? This course covers the important aspects that you need to know to solve business problems through linear regression.
Although most courses only focus on teaching how to run the analysis, this course emphasizes what happens before and after analysis such as having the right data and performing preprocessing on it. You’ll also be able to judge how good your model is and interpret the results to help your business. As you progress, you will learn how to identify problems in your business and solve them using linear regression techniques. In addition to this, you’ll gain the knowledge you need to create a linear regression model in R and analyze its results.
By the end of this course, you will be equipped with the skills you need to effectively use linear regression for predictive machine learning and create robust models.
Learn
- Identify the business problem and solve it using linear regression techniques
- Create a linear regression model in R and analyze its results
- Become well-versed with machine learning concepts
- Gain knowledge of data collection and data preprocessing for machine learning linear regression problems
- Explore advanced linear regression techniques using R’s glmnet package
Table of Contents
Introduction
1 Welcome to the course!
2 Course contents
Basics of Statistics
3 Types of Data
4 Types of Statistics
5 Describing the data graphically
6 Measures of Centers
7 Measures of Dispersion
Getting started with R and R studio
8 Installing R and R studio
9 Basics of R and R studio
10 Packages in R
11 Inputting data part 1 – Inbuilt datasets of R
12 Inputting data part 2 – Manual Data Entry
13 Inputting data part 3 – Importing from CSV or Text files
14 Creating Barplots in R
15 Creating Histograms in R
Introduction to Machine Learning
16 Introduction to Machine Learning
17 Building a Machine Learning model
Data Pre-processing
18 Gathering Business Knowledge
19 Data Exploration
20 The Data and the Data Dictionary
21 Importing the dataset into R
22 Univariate Analysis and EDD
23 EDD in R
24 Outlier Treatment
25 Outlier Treatment in R
26 Missing Value imputation
27 Missing Value imputation in R
28 Seasonality in Data
29 Bi-variate Analysis and Variable Transformation
30 Variable transformation in R
31 Non-Usable Variables
32 Dummy variable creation – Handling qualitative data
33 Dummy variable creation in R
34 Correlation Matrix and cause-effect relationship
35 Correlation Matrix in R
Linear Regression Model
36 The problem statement
37 Basic equations and Ordinary Least Squared (OLS) method
38 Assessing Accuracy of predicted coefficients
39 Assessing Model Accuracy – RSE and R squared
40 Simple Linear Regression in R
41 Multiple Linear Regression
42 The F – statistic
43 Interpreting result for categorical Variable
44 Multiple Linear Regression in R
45 Test-Train split
46 Bias Variance trade-off
47 Test-Train split in R
Regression models other than OLS
48 Linear models other than OLS
49 Subset Selection techniques
50 Subset selection in R
51 Shrinkage methods – Ridge Regression and The Lasso
52 Ridge regression and Lasso in R
53 Heteroscedasticity
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