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  • How to implement different machine learning classification algorithms using matlab.
  • How to impplement different machine learning clustering algorithms using matlab
  • How to proprocess 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

 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 worldwide. 

Below is the brief outline of this course. 

Segment 1: Introduction to course and say hi to MATLAB

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

  • 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
  • Data Scientists, Researchers, Entrepreneurs, Instructors, College Students, Engineers and Programmers
  • Anyone who want to analyze the data
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  • Section 1 : Introduction 2 Lectures 00:13:38

    • Lecture 1 :
    • Lecture 2 :
    • Introduction to Matlab
  • Section 2 : Data Preprocessing 11 Lectures 01:09:05

    • Lecture 1 :
    • Section Introduction
    • Lecture 2 :
    • Importing the data into MATLAB
    • Lecture 3 :
    • Code and Data
    • Lecture 4 :
    • Handling Missing Data (Part 1)
    • Lecture 5 :
    • Handling Missing Data (Part 2)
    • Lecture 6 :
    • Feature scaling
    • Lecture 7 :
    • Outliers (Part 1)
    • Lecture 8 :
    • Outliers (Part 2)
    • Lecture 9 :
    • Dealing with Categorical Data (Part 1)
    • Lecture 10 :
    • Dealing with Categorical Data (Part 2)
    • Lecture 11 :
    • Your Data Preproprocessing Timplate
  • Section 3 : Classification 1 Lectures

    • Lecture 1 :
    • Code and Data
  • Section 4 : K-Nearest Neighbor 8 Lectures 01:05:04

    • Lecture 1 :
    • KNN Intuition
    • Lecture 2 :
    • KNN in matlab (Part 1)
    • Lecture 3 :
    • KNN in MATLAB (Part 2)
    • Lecture 4 :
    • Visualizing the Decision Boundaries of KNN
    • Lecture 5 :
    • Explaining the code of visualization
    • Lecture 6 :
    • Here is our classification template
    • Lecture 7 :
    • Customization options (part 1)
    • Lecture 8 :
    • Customization options (part 2)
  • Section 5 : Naive Bayesain 4 Lectures 00:36:52

    • Lecture 1 :
    • Intuition of Naive Bayesain (Part 1)
    • Lecture 2 :
    • Intuition of Naive Bayesain (Part 2)
    • Lecture 3 :
    • Naive Bayesain in Matlab
    • Lecture 4 :
    • Customization Options of Naive Bayesain In MATLAB
  • Section 6 : Decision Tree 4 Lectures 00:33:38

    • Lecture 1 :
    • Decision Trees Intuition
    • Lecture 2 :
    • Decision tree in matlab
    • Lecture 3 :
    • Visualizing the decision tree using the view function
    • Lecture 4 :
    • Customization Options for Decision Trees
  • Section 7 : SVM 4 Lectures 00:38:17

    • Lecture 1 :
    • SVM Intuition (Part 1)
    • Lecture 2 :
    • Kernel SVM Intuition
    • Lecture 3 :
    • SVM in MATLAB
    • Lecture 4 :
    • Customization Options for SVM
  • Section 8 : Discriminant analysis 3 Lectures 00:22:19

    • Lecture 1 :
    • Discriminant Analysis Intuition
    • Lecture 2 :
    • Discriminant Analysis in MATLAB
    • Lecture 3 :
    • Customization Options for Discriminant Analysis
  • Section 9 : Ensembles 3 Lectures 00:36:13

    • Lecture 1 :
    • Ensembles Intuition
    • Lecture 2 :
    • Ensembles in matlab
    • Lecture 3 :
    • Customization Options for Ensembles
  • Section 10 : Evaluation 5 Lectures 00:57:00

    • Lecture 1 :
    • Confusion Matrix
    • Lecture 2 :
    • Validation_methods
    • Lecture 3 :
    • Validation methods (Part 1)
    • Lecture 4 :
    • Validation methods (Part2)
    • Lecture 5 :
    • Evaluation
  • Section 11 : Clustering 1 Lectures

    • Lecture 1 :
    • Code and Data
  • Section 12 : K-means 4 Lectures 00:55:49

    • Lecture 1 :
    • K-Means Clustering Intuition
    • Lecture 2 :
    • Choosing the number of clusters
    • Lecture 3 :
    • K-means clustering in MATLAB (Part 1)
    • Lecture 4 :
    • K-means clustering in MATLAB (Part 2)
  • Section 13 : Hierarchical 3 Lectures 00:44:47

    • Lecture 1 :
    • Hierarchical Clustering Intuition (Part 1)
    • Lecture 2 :
    • Hierarchical Clustering Intuition (Part 2)
    • Lecture 3 :
    • HC in matlab
  • Section 14 : Dimensionality reduction 4 Lectures 00:38:24

    • Lecture 1 :
    • Code and Data
    • Lecture 2 :
    • PCA
    • Lecture 3 :
    • PCA in MATLAB (Part 1)
    • Lecture 4 :
    • PCA in MATLAB (Part 2)
  • Section 15 : Project 5 Lectures 00:41:30

    • Lecture 1 :
    • Code and Data
    • Lecture 2 :
    • Project Discription
    • Lecture 3 :
    • Customizing code templates for completing Task 1 and 2 (Part 1)
    • Lecture 4 :
    • Customizing code templates for completing Task 1 and 2 (Part 2)
    • Lecture 5 :
    • Customizing code templates for completing Task 3, 4 and 5
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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.
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