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  • Reading time:7 mins read




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What you will learn

 

 

Machine Learning Algorithms in Excel

 

Data Clustering such as Fine Classing and Weight of Evidence

 

Data Wrangling and Transformation

 

Mathematics for Machine Learning

 

Credit Risk Modelling and Validation

 

Build a complete Credit Risk Model with Machine Learning by Using Excel

 

Impress interviews by adding a special skill in your Resume

 

Description

 

Hi and welcome to the Machine Learning with Excel course,

Machine Learning is shaping our everyday lives and it one of the most important features of innovations

in technology. The purpose of this course is to equip you with the newest methods that are applied in

Machine Learning by Using Microsoft Excel. It will introduce you to a different way of thinking about

data science and machine learning. This is a good way to start a career in Machine Learning since you

will understand some initial concepts and gain some hands-on experience on it. I am extremely happy

share with you everything that I know about Machine Learning with Excel. I promise you it is going to be

worth it and you will gain a valuable set of knowledge and skills by attending this course.


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This is the only course in Udemy where Machine Learning is applied in Microsoft Excel. The reason why

we chose to go with Excel is because we know that many of you are already familiar with it. We will start

from ground zero and together we will continuously develop new skills from the beginning to the end of

this course. In this course together we will implement a complete data science project from start to

finish using Credit Risk Data. For this course we have data for around 40,000 consumers and a lot of

characteristics about them such as: their level of education, their age, their marital status, where they

live, if they own a home, and other useful details. We will get our hands dirty with these data and

explore them in depth and you can practice all this on your own too. Moreover, you will gain access to

valuable resources such as lectures, homework, quizzes, slides as well as some literature review in

regard to the modelling approaches. Let’s go ahead now and see how the course structure looks like!

 

English
language

 

Content

 
Introduction
Introduction
Data Science
Theoretical Background for Credit Risk
What is Credit Risk
Expected Losses
Data Overview
Data Overview
What type of Data do we have
Preprocessing Data
Combining Data
How do we combine data when we have labels instead of Numbers
Creating Gender Dummy Variables
How Many Genders do we have in our Data
Creating Ownership Dummy Variables
What function do we use when we create dummy variables
Relationship Status Dummy Variables
Coding of the Dependent Variable
Weight of Evidence Male and Female
Weight of Evidence – Rural vs Urban
Weight of Evidence- School Level
Weight of Evidence- Work Sector and Residence
Weight of Evidence – Relationship Status
Continuous Variables
Continuous Varisbes
.Coding of Variables Age in Years
Coding of variables: Maturity of the Loan
Multicollinearity of Variables
Weight of Evidence for Age in Years
Weight of Evidence Maturity
Choosing Variables for Machine Learning Algorithm
Choosing Variables for Machine Learning Algorithm
Installing Real Statistics
Running the Machine Learning Algorithm
The meaning of Results
Validation of Results
Applying the results into test dataset