Machine Learning Basics with R Studio by Start-Tech Academy

Machine Learning Basics with R Studio

Master the basics of Machine Learning in Python and use Regression Models to solve business problems

What's included?

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Contents

Introduction
Welcome to the course!
3 mins
Course contents
6 mins
Course Resourses
8.55 MB
Basics of Statistics
Types of Statistics
3 mins
Describing data Graphically
12 mins
Types of Data
5 mins
Measures of Centers
8 mins
Measures of Dispersion
5 mins
Getting started with R and R studio
Installing R and R studio
6 mins
Basics of R and R studio
11 mins
Packages in R
11 mins
Inputting data part 1 - Inbuilt datasets of R
5 mins
Inputting data part 2 - Manual data entry
4 mins
Inputting data part 3 - Importing from CSV or Text files
7 mins
Creating Barplots in R
14 mins
Creating Histograms in R
7 mins
Introduction to Machine Learning
Building a Machine Learning Model
9 mins
Introduction to Machine Learning
17 mins
Data Preprocessing
Gathering Business Knowledge
4 mins
Data Exploration
4 mins
The Dataset and the Data Dictionary
8 mins
Importing the dataset into R
3 mins
Univariate analysis and EDD
4 mins
EDD in R
13 mins
Outlier Treatment
5 mins
Outlier Treatment in R
5 mins
Missing Value Imputation
4 mins
Missing Value imputation in R
4 mins
Seasonality in Data
4 mins
Bi-variate analysis and Variable transformation
17 mins
Variable transformation in R
10 mins
Non-usable variables
5 mins
Dummy variable creation: Handling qualitative data
5 mins
Dummy variable creation in R
6 mins
Correlation Analysis
11 mins
Correlation Matrix in R
9 mins
Linear Regression
Basic Equations and Ordinary Least Squares (OLS) method
9 mins
The Problem Statement
2 mins
Assessing accuracy of predicted coefficients
15 mins
Assessing Model Accuracy: RSE and R squared
8 mins
Simple Linear Regression in R
8 mins
Multiple Linear Regression
5 mins
The F - statistic
9 mins
Interpreting results of Categorical variables
6 mins
Multiple Linear Regression in R
8 mins
Test-train split
10 mins
Bias Variance trade-off
7 mins
Test-Train Split in R
9 mins
Other Linear Models
Linear models other than OLS
5 mins
Subset selection techniques
12 mins
Subset Selection in R
8 mins
Shrinkage methods: Ridge and Lasso
8 mins
Ridge and Lasso in R
13 mins
Extras: Heteroscedasticity
3 mins