About This Course
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.
30 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 using MATLAB.
|Section 1 - Introduction To Course And MATLAB|
|Introduction To Course||00:00:00|
|Introduction To MATLAB||00:00:00|
|Importing Dataset Into MATLAB||00:00:00|
|Section 2 - Handling Missing Values|
|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|
|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|