MATLAB : Data Preprocessing For Machine Learning
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.
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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Section 1 - Introduction To Course And MATLAB
Introduction To Course
Introduction To MATLAB
Importing Dataset Into MATLAB
Section 2 - Handling Missing Values
Using Mean And Mode
Considering As A Special Value
Class Specific Mean And Mode
Random Value Imputation
Section 3 - Dealing With Categorical Variables
Categorical Data With No Order
Categorical Data With Order
Frequency Based Encoding
Target Based Encoding
Section 4 - Outlier Detection
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
Section 5 - Feature Scaling And Data Discretization
Discretization Using Equal Width Binning
Discretization Using Equal Frequency Binning
Section 6 - Project: Selecting The Right Method For Your Data
Selecting the right method – Part 1
Selecting the right method – Part 2
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