**Machine Learning for Beginners: Linear Regression Model in R**

English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 6h 13m | 1.91 GB

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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