Linear Discriminant Analysis | LDA | Discriminant Analysis Tutorial | Machine Learning Tutorial Hello and welcome to great learning's Machine Learning tutorial. Linear Discriminant Analysis, used for dimension or feature reduction of data this means, that this is used as a pre-processing step in machine learning and also finds its application in pattern classification. Linear Discriminant Analysis can be used for the both purposes.
Discriminant analysis finds its application in finance for example approval of bank loan application in marketing for predicting customer trends in medical science for example image recognition etc. It is the state of art business approach that can drive successful strategies and can propel decision making processes to greater heights.
Data Scientists and business analysts can use this tool and use its results to compose new processes and design that may be beneficial for the organization.
This video demonstrates and helps us to get information about the application of Linear Discriminant analysis and the core mathematics behind it which will help us to design problems and apply discriminant analysis to solve the problems existing in corporates and various industries.
#lineardiscriminantanalysis #lda

What is Discriminant analysis?
Discriminant analysis is the appropriate tool to use when the dependent variable is categorical(nominal), and the independent variables are interval scaled or continuous. Of course, you can have more than two groups in the categorical dependent variable.
The objective of discriminant analysis is to use the information from the independent variables to achieve the clearest possible separation or discrimination among groups.

Goals of Discriminant analysis
- Profiling
- Differentiation
- Classification

Topics Covered:
00:00:00 Introduction
00:01:44 Agenda
00:03:45 Discriminant analysis
00:06:00 Goals of Discriminant analysis
00:07:26 Exercise on the application of Fisher’s discriminant analysis
01:28:23 Summary

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