Machine Learning For Data Science Using MATLAB

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    Online

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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 courseSegment 2: Data preprocessing Segment 3: Classification Algorithms in MATLABSegment 4: Clustering Algorithms in MATLABSegment 5: Dimensionality ReductionSegment 6: Project: Malware AnalysisYour Benefits and Advantages: You will be sure of receiving quality contents
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.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 Regrads,Dr. Nouman AzamMore Benefits and Advantages: ✔ You receive knowledge from an experienced instructor (Dr. Nouman Azam) who is the creator of five courses.✔ The titles of these courses are Complete MATLAB Tutorial: Go from Beginner to Pro

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Online

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About this course

How to implement different machine learning classification algorithms using matlab.
How to implement 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 

Data Scientists, Researchers, Entrepreneurs, Instructors, College Students, Engineers and Programmers
Anyone who want to analyze the data

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This centre's achievements

2021

All courses are up to date

The average rating is higher than 3.7

More than 50 reviews in the last 12 months

This centre has featured on Emagister for 6 years

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Course programme

Introduction to course and MATLAB 2 lectures 13:37 Course introduction MATLAB essentials for the course Introduction to course and MATLAB 2 lectures 13:37 Course introduction MATLAB essentials for the course Course introduction Course introduction Course introduction Course introduction MATLAB essentials for the course MATLAB essentials for the course MATLAB essentials for the course MATLAB essentials for the course Data Preprocessing 11 lectures 01:09:00 Code and Data 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 Data Preprocessing 11 lectures 01:09:00 Code and Data 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 Code and Data Code and Data Code and Data Code and Data Section Introduction Section Introduction Section Introduction Section Introduction Importing the Dataset Importing the Dataset Importing the Dataset Importing the Dataset Removing Missing Data (Part 1) Removing Missing Data (Part 1) Removing Missing Data (Part 1) Removing Missing Data (Part 1) Removing Missing Data (Part 2) Removing Missing Data (Part 2) Removing Missing Data (Part 2) Removing Missing Data (Part 2) Feature Scaling Feature Scaling Feature Scaling Feature Scaling Handling Outliers (Part 1) Handling Outliers (Part 1) Handling Outliers (Part 1) Handling Outliers (Part 1) Handling Outliers (Part 2) Handling Outliers (Part 2) Handling Outliers (Part 2) Handling Outliers (Part 2) Dealing with Categorical Data (Part 1) Dealing with Categorical Data (Part 1) Dealing with Categorical Data (Part 1) Dealing with Categorical Data (Part 1) Dealing with categorical data (Part 2) Dealing with categorical data (Part 2) Dealing with categorical data (Part 2) Dealing with categorical data (Part 2) Your Preprocessing Template Your Preprocessing Template Your Preprocessing Template Your Preprocessing Template Classification 1 lecture 00:00 Classification - Code and Data Classification 1 lecture 00:00 Classification - Code and Data Classification - Code and Data Classification - Code and Data Classification - Code and Data Classification - Code and Data K-Nearest Neighbor 8 lectures 01:15:32 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 K-Nearest Neighbor 8 lectures 01:15:32 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 KNN Intuition KNN Intuition KNN Intuition KNN Intuition KNN in MATLAB (Part 1) KNN in MATLAB (Part 1) KNN in MATLAB (Part 1) KNN in MATLAB (Part 1) KNN in MATLAB (Part 2) KNN in MATLAB (Part 2) KNN in MATLAB (Part 2) KNN in MATLAB (Part 2) Visualizing the Decision Boundaries of KNN Visualizing the Decision Boundaries of KNN Visualizing the Decision Boundaries of KNN Visualizing the Decision Boundaries of KNN Explaining the code for visualization Explaining the code for visualization Explaining the code for visualization Explaining the code for visualization Here is our classification template Here is our classification template Here is our classification template Here is our classification template How to change default options and customize classifiers How to change default options and customize classifiers How to change default options and customize classifiers How to change default options and customize classifiers Customization options for KNN Customization options for KNN Customization options for KNN Customization options for KNN Naive Bayes 4 lectures 36:49 Naive Bayesain Intuition (Part 1) Naive Bayesain Intuition (Part 2) Naive Bayesain in MATLAB Customization Options for Naive Bayesain Naive Bayes Evaluating Classifiers in MATLAB Evaluating Classifiers in MATLAB Clustering 1 lecture 00:00 Clustering - Code and Data Clustering 1 lecture 00:00 Clustering - Code and Data Clustering - Code and Data Clustering - Code and Data Clustering - Code and Data ...

Additional information

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

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Machine Learning For Data Science Using MATLAB

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