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About This Course

Who this course is for:

  • This course is for you if you want to fully equip 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.
  • 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 machine learning algorithms. 

What you’ll learn: 

  • How to effectively preprocess data before analysis.
  • How to implement different preprocessing methods using MATLAB
  • Take away code templates for quickly preprocessing your data
  • Decide which method choose for your dataset


  • No prior MATLAB knowledge is required to take this course

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.

10 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.

Get started today and learn more about data preprocessing.

Course Curriculum

4h 13m

Section 1 - Introduction To Course And MATLAB
Downloadable Materials 00:00:00
Introduction To Course 00:00:00
Introduction To MATLAB 00:00:00
Importing Dataset Into MATLAB 00:00:00
Section 2 - Handling Missing Values
Deletion Strategies 00:00:00
Using Mean And Mode 00:00:00
Considering As A Special Value 00:00:00
Class Specific Mean And Mode 00:00:00
Random Value Imputation 00:00:00
Section 3 - Dealing With Categorical Variables
Categorical Data With No Order 00:00:00
Categorical Data With Order 00:00:00
Frequency Based Encoding 00:00:00
Target Based Encoding 00:00:00
Section 4 - Outlier Detection
3 Sigma Rule With Deletion Strategy 00:00:00
3 Sigma Rule With Filling Strategy 00:00:00
Box Plots And Iterquartile Rule 00:00:00
Class Specific Box Plots 00:00:00
Histograms For Outliers 00:00:00
Local Outlier Factor – Part 1 00:00:00
Local Outlier Factor – Part 2 00:00:00
Outliers In Categorical Variables 00:00:00
Section 5 - Feature Scaling And Data Discretization
Feature Scalling 00:00:00
Discretization Using Equal Width Binning 00:00:00
Discretization Using Equal Frequency Binning 00:00:00
Section 6 - Project: Selecting The Right Method For Your Data
Selecting the right method – Part 1 00:00:00
Selecting the right method – Part 2 00:00:00