Machine Learning for Data Science Course in MATLAB

Machine Learning for Data Science using Matlab

Learn to implement classification and clustering algorithms using MATLAB wi

Created by : Rated :4.3 28857 views

Last Updated: 2024-11-19 05:11:13 English Course Type: Self Learning

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

Section 1 : Introduction 2 Lectures 00:13:38
Lecture 1 :
Introduction to course Preview
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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Dr. Nouman Azam,

93583 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.