MATLAB : Machine Learning For Data Science

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This course is designed for those interested to learn the basics of machine learning using MATLAB and know how to implement and solve data science problems with it.

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About This CourseBeginner

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 math. 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 scratch. We will immediately start coding after a couple of introductory tutorials and we try to keep the theory to bare minimal. 

Below is a 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

All the coding will be done in MATLAB which is one of the fundamental programming languages for engineering and science students and is frequently used by top data science research groups worldwide.

Our Promise to You

By the end of this course, you will have learned about machine learning using MATLAB.

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Get started today and learn more about the machine learning using MATLAB.

Course Curriculum

Course Sections

Downloadable Material

Course Introduction

MATLAB Essentials For The Course

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 Preprocessing Template

K – Nearest Neighbor Intuition

K – Nearest Neighbor In MATLAB – Part 1

K – Nearest Neighbor In MATLAB – Part 2

Visualizing The Decision Boundaries Of K – Nearest Neighbor

Explaining The Code For Visualization

Here Is Our Classification Template

How To Change Default Options And Customize Classifiers

Customization Options For K – Nearest Neighbor

Naive Bayesain Intuition – Part 1

Naive Bayesain Intuition – Part 2

Naive Bayesain In MATLAB

Customization Options For Naive Bayesain

Decision Trees Intuition

Decision Trees In MATLAB

Visualizing Decision Trees Using The View Function

Customization Options For Decision Trees

Support Vector Machines Intuition

Kernel Support Vector Machines Intuition

Support Vector Machines In MATLAB

Customization Options For Support Vector Machines

Discriminant Analysis Intuition

Discriminant Analysis In MATLAB

Customization Options For Discriminant Analysis

Ensembles Intuition

Ensembles In MATLAB

Customization Options For Ensembles

Evaluating Classifiers: Confusion Matrix (Theory)

Validation Methods (Theory)

Validation Methods In MATLAB – Part 1

Validation Methods In MATLAB – Part 2

Evaluating Classifiers In MATLAB

K-Means Clustering Intuition

Choosing The Number Of Clusters

K-Means In MATLAB – Part 1

K-Means In MATLAB – Part 2

Hierarchical Clustering Intuition – Part 1

Hierarchical Clustering Intuition – Part 2

Hierarchical Clustering In MATLAB

Principal Component Analysis

Principal Component Analysis In MATLAB – Part 1

Principal Component Analysis In MATLAB – Part 2

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, Task 4, And 5

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