Course description

Basic Course Description 

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 algorithmsBy 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.

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It's time to take Action!

Click the "Take This Course" button at the top right now!

...Time is limited and Every second of every day is valuable...

We are excited to see you in the course!

Best Regards,

Dr. Nouman Azam

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More Benefits and Advantages: 

✔ You receive knowledge from an experienced instructor (Dr. Nouman Azam) who is the creator of many courses on Udemy in the MATLAB niche. 

✔ The titles of these courses are 

  • Machine Learning for Data Science using MATLAB

  • Machine Learning Classification Algorithms using MATLAB

  • Complete MATLAB Tutorial: Go from Beginner to Pro

  • MATLAB App Designing: The Ultimate Guide for MATLAB Apps

  • Create Apps in MATLAB with App Designer (Codes Included)

  • Advance MATLAB Data Types and Data Structures


Who this course is for:

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


Requirements:

  • MATLAB 2017a or higher 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.


What you'll learn:

  • How to effectively pro-process 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

Course curriculum

  • 1
    Introduction to the course and MATLAB
  • 2
    Handling Missing Values
    • Deletion strategies
    • Using mean and mode
    • Adding special values
    • Class specific mode and mean
    • Random value imputation
  • 3
    Dealing with Categorical Variables
    • Categorical data with no order
    • Categorical data with order
    • Frequency encoding
    • Target based encoding
  • 4
    Outlier Detection
    • 3 sigma rule with deletion strategy
    • 3 sigma rule with filling strategy
    • Histograms for outliers
    • Box plots (Part 1)
    • Box plots (Part 2)
    • LOF (Part 1)
    • LOF (Part 2)
    • Outliers in categorical variables
  • 5
    Feature Scalling and Data Discretization
    • Feature scaling
    • Equal width binning
    • Equal frequency binning
  • 6
    Project: Selecting Techniques for your Dataset
    • Selecting the right method (Part 1)
    • Selecting the right method (Part 2)

Meet your instructor!

Nouman Azam
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 platforms. 

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 techniques using MATLAB is one of my favorite 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.

Take this course today!

"Data Preprocessing for Machine Learning Using MATLAB"

Bundles including this course!