Data Preprocessing for Machine Learning using MATLAB
Learn to implement commonly used Data Preprocessing Techniques in MATLAB with practical examples, project and datasets
Created by Nouman Azam, Last Updated 27-Jan-2020, Language:English
What Will I Get ?
- How to effectively proprocess 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
- 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.
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 peformance 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 world wide.
Below is the 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 Scalling and Data Discretization
Segment 6: Project: Selecting Techniques for your Dataset
Your Benefits and Advantages:
If you do not find the course useful, you are covered with 30 day money back guarantee, full refund, no questions asked!
You will be sure of receiving quality contents since the instructors has already many courses in the MATLAB on udemy.
You have lifetime access to the course.
You have instant and free access to any updates i add to the course.
You have access to all Questions and discussions initiated by other students.
You will receive my support regarding any issues related to the course.
Check out the curriculum and Freely available lectures for a quick insight.
Who this course is for:
- Students, Entrepreneurs, Researchers, Instructors, Engineers, Programmers, Simulators
- Anyone who want to analyze the data
Introduction to course and MATLAB
Introduction to coursePreview00:04:24
Introduction to MATLABPreview00:08:26
Importing Dataset into MATLABPreview00:07:34
Handling Missing Values
Dealing with Categorical Variables
Box plots and iterquartile rulePreview00:08:18
Feature Scaling and Data Discretization
Project: Selecting the Right Method for your Data
Your MATLAB Professor
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.