MATLAB : Data Preprocessing For Machine Learning

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This course is designed for those interested to learn the basics of data preprocessing using MATLAB and be able to figure out how to further improve the performance of the machine learning algorithms.

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About This CourseBeginner

This course is for you if you want to fully equipped yourself with the art of applied machine learning using MATLAB and if you want to apply the most commonly used data preprocessing techniques without having to learn all the complicated math.

Additionally, this course is also for you if you have had previous hours and hours of machine learning implementation but could never figure out how to further improve the performance of the machine learning algorithms. 

By the end of this course, you will have at your fingertips, a vast variety of most commonly used data preprocessing techniques that you can use instantly to maximize your insight into your data set.

The approach in this course is very practical and we will start everything from scratch. We will immediately start coding after a couple of introductory tutorials and we try to keep the theory to bare minimal. All the coding will be done in MATLAB which is one of the fundamental programming languages for engineering and science students and is frequently used by top data science research groups worldwide.

Below is a brief outline of this course:

Segment 1: Introduction To Course And MATLAB

Segment 2: Handling Missing Values

Segment 3: Dealing With Categorical Variables

Segment 4: Outlier Detection

Segment 5: Feature Scaling And Data Discretization

Segment 6: Project: Selecting Techniques For Your Dataset

Our Promise to You

By the end of this course, you will have learned about data preprocessing using MATLAB.

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Course Curriculum

Course Sections

Downloadable Materials

Introduction To Course

Introduction To MATLAB

Importing Dataset Into MATLAB

Deletion Strategies

Using Mean And Mode

Considering As A Special Value

Class Specific Mean And Mode

Random Value Imputation

Categorical Data With No Order

Categorical Data With Order

Frequency Based Encoding

Target Based Encoding

3 Sigma Rule With Deletion Strategy

3 Sigma Rule With Filling Strategy

Box Plots And Iterquartile Rule

Class Specific Box Plots

Histograms For Outliers

Local Outlier Factor – Part 1

Local Outlier Factor – Part 2

Outliers In Categorical Variables

Feature Scalling

Discretization Using Equal Width Binning

Discretization Using Equal Frequency Binning

Selecting the right method – Part 1

Selecting the right method – Part 2

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