Learn Data Preprocessing in MATLAB for Machine Learning

Data Preprocessing for Machine Learning using MATLAB

Learn to implement commonly used Data Preprocessing Techniques in MATLAB

Created by : Rated :4.7 64721 views

Last Updated: 2024-11-19 04:11:13 English Course Type: Self Learning

How to effectively proprocess data before analysis.
How to implement different preprocessing methods using matlab.
Take away code templates for quickly preprocessing your data

This course is for you if you want to fully equip yourself with the art of applied machine learning using MATLAB. This course is also for you if you want to apply the most commonly used data preprocessing techniques without having to learn all the complicated maths. 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 very 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 engineer and science students and is frequently used by top data science research groups worldwide.

MATLAB 2017a or heigher version. No prior knowledge of MATLAB is required
In version below 2017a there might be some functions that will not work
We cover everything from scratch and therefore do not require any prior knowledge of MATLAB

Students, Entrepreneurs, Researchers, Instructors, Engineers, Programmers, Simulators
Anyone who want to analyze the data

Section 1 : Introduction to course and MATLAB 3 Lectures 00:20:27
Lecture 1 :
Introduction to course Preview
Lecture 2 :
Introduction to MATLAB
Lecture 3 :
Importing Dataset into MATLAB
Section 2 : Handling Missing Data 6 Lectures 00:53:19
Lecture 1 :
Code and Data
Lecture 2 :
Deletion Strategies
Lecture 3 :
Using mean and mode
Lecture 4 :
Adding Special Value
Lecture 5 :
Class Specific Mode Mean
Lecture 6 :
Random Value Imputation
Section 3 : Dealing with Categorical data 5 Lectures 00:38:30
Lecture 1 :
Code and Data
Lecture 2 :
Categorical data with no order
Lecture 3 :
Categorical data with order
Lecture 4 :
Frequency Encoding
Lecture 5 :
Target Based Encoding
Section 4 : Outliers 9 Lectures 01:21:38
Lecture 1 :
Code and Data
Lecture 2 :
3 sigma rule with deletion dtrategy
Lecture 3 :
3 sigma rule with filling strategy
Lecture 4 :
Histograms for outliers
Lecture 5 :
Box Plots (Part 1)
Lecture 6 :
Box Plots (Part 2
Lecture 7 :
LOF (Part 1)
Lecture 8 :
LOF (Part 2)
Lecture 9 :
Outliers in categorical variables
Section 5 : Feature Scalling and Discretization 4 Lectures 00:32:16
Lecture 1 :
Code and Data
Lecture 2 :
Feature Scalling
Lecture 3 :
Equal Width Binning
Lecture 4 :
Equal Frequency Binning
Section 6 : Project-Selecting Approperiate Methods 3 Lectures 00:27:53
Lecture 1 :
Code and Data
Lecture 2 :
Selecting the right method (Part 1)
Lecture 3 :
Selecting the right method (Part 2)

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Dr. Nouman Azam,

93574 Course Views

2 Courses

I am Dr. Nouman Azam and i am Assistant Professor in Computer Science. I teach online courses related to MATLAB Programming to more than 10,000 students on different online plateforms. The focus in these courses is to explain different aspects of MATLAB and how to use them effectively in routine daily life activities. In my courses, you will find topics such as MATLAB programming, designing gui's, data analysis and visualization. Machine learning techinques using MATLAB is one of my favourate topic. During my research career i explore the use of MATLAB in implementing machine learning techniques such as bioinformatics, text summarization, text categorization, email filtering, malware analysis, recommender systems and medical decision making.