Uthena

Course description

Basic Course Description 

This course is for you if you want to have a real feel of the Machine Learning 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 theory but could never got a change or figure out how to implement and solve data science problems with it. 

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
  • Segment 2: Data preprocessing 
  • Segment 3: Classification Algorithms in MATLAB
  • Segment 4: Clustering Algorithms in MATLAB
  • Segment 5: Dimensionality Reduction
  • Segment 6: Project: Malware Analysis

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Your Benefits and Advantages: 

  • 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 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 five courses on Udemy in the MATLAB niche. 

✔ The titles of these courses are 

  • Complete MATLAB Tutorial: Go from Beginner to Pro

  • MATLAB App Designing: The Ultimate Guide for MATLAB Apps

  • Machine Learning Classification Algorithms using MATLAB

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

  • Advance MATLAB Data Types and Data Structures


Who this course is for:

  • Data Scientists, Researchers, Entrepreneurs, Instructors, College Students, Engineers and Programmers
  • 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


What you'll learn:

  • How to implement different machine learning classification algorithms using Matlab.
  • How to implement different machine learning clustering algorithms using Matlab.
  • How to pro-process data before analysis.
  • When and how to use dimensionality reduction.
  • Take away code templates.
  • Visualization results of algorithms
  • Decide which algorithm to choose for your dataset

Course curriculum

  • 1

    Introduction to the course and MATLAB

  • 2

    Data preprocessing

    • Section introduction

    • Importing the dataset

    • Removing missing data (Part 1)

    • Removing missing data (Part 2)

    • Feature scaling

    • Handling outliers (Part 1)

    • Handling outliers (Part 2)

    • Dealing with categorical data (Part 1)

    • Dealing with categorical data (Part 2)

    • Your preproprocessing template
  • 3

    K-Nearest Neighbor

    • KNN intuition

    • KNN in MATLAB (Part 1)

    • KNN in MATLAB (Part 2)

    • Visualizing the decision boundaries of KNN

    • Explaining the code for visualization

    • Here is our classification template

    • How to change default options and customize classifiers

    • Customization options for KNN
  • 4

    Naive Bayes

    • Naive Bayesain Intuition (Part 1)

    • Naive Bayesain Intuition (Part 2)

    • Naive Bayesain in MATLAB

    • Customization options for Naive Bayesain
  • 5

    Decision Trees

    • Decision trees intuition

    • Decision trees in MATLAB

    • Visualizing decision trees using the view function

    • Customization options for decision trees
  • 6

    Support Vector Machines

    • SVM Intuition

    • Kernel SVM Intuition

    • SVM in MATLAB

    • Customization options for SVM
  • 7

    Discriminant Analysis

    • Discriminant Analysis Intuition

    • Discriminant analysis in MATLAB

    • Customization options for discriminant analysis
  • 8

    Ensembles

    • Ensembles Intuition

    • Ensembles in MATLAB

    • Customization options for ensembles
  • 9

    Performance Evaluation

    • Evaluating Classifiers: Confusion matrix (theory)

    • Validation methods (theory)

    • Validation methods in MATLAB (Part 1)

    • Validation methods in MATLAB (Part 2)

    • Evaluating Classifiers in MATLAB
  • 10

    K-Means

    • K-Means Clustering Intuition

    • Choosing the number of clusters

    • K-means in MATLAB (Part 1)

    • K-means in MATLAB (Part 2)
  • 11

    Hierarchical Clustering

    • Hierarchical Clustering Intuition (Part 1)

    • Hierarchical Clustering Intuition (Part 2)

    • Hierarchical Clustering in MATLAB
  • 12

    Dimensionality Reduction

    • Principal Component Analysis

    • PCA in MATLAB (Part 1)

    • PCA in MATLAB (Part 2)
  • 13

    Project: Malware Analysis

    • Problem description

    • Customizing code templates for completing Task 1 and 2 (Part 1)

    • Customizing code templates for completing Task 1 and 2 (Part 2)

    • Customizing code templates for completing Task 3, 4 and 5

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!

"Machine Learning for Data Science using MATLAB"

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