Machine Learning Classification Algorithms Using MATLAB

Take a Deeper Look at How Machines Classify Information

As the name suggests, classification algorithms are what allow computers to well...classify new observations, like how your inbox decides which incoming emails are spam or how Siri recognizes your voice. This course will show you how to implement classification algorithms using MATLAB, one of the most powerful tools inside a data scientist's toolbox. Following along step-by-step, you'll start with the MATLAB basics then move on to working with key classification algorithms, like K-Nearest Neighbor, Discriminant Analysis, and more as you come to grips with this machine learning essential. Upon completion of this course, and all courses included in the bundle, you'll also receive a certification of completion validating your new skills! This is especially useful for including in your portfolio or resume, so future employers can feel confident in your skill set.

Learn how to confidently implement machine learning algorithms using MATLAB

Understand how to perform a meaningful analysis of your data & share it w/ others

Instructor

Nouman Azam received his Ph.D. Degree in Computer Sceince from the University of Regina in 2014. Prior to that, he completed his M.Sc. in Computer Software Engineering from the National University of Sciences and Technology, Pakistan and earned his Bachelors in Computer Sciences from the National University of Computer and Emerging Sciences, Pakistan in 2007 and 2005 respectively

He is the creator of six online MATLAB courses. He has extensive knowledge of tools, such as MATLAB, QTSpim, C++, Java, LaTeX and other academic resources used for teaching and instructing purposes. Overall, he has over 10 years of teaching and relevant experience at undergraduate and graduate level.

Important Details

Length of time users can access this course: lifetime

Access options: desktop and mobile

Certification of completion not included

Redemption deadline: redeem your code within 30 days of purchase

Experience level required: beginner

Requirements

Internet required

Includes a certification of completion

Course Outline

Course and Instructor Introduction

Applications of Machine Learning (1:35)

Why use MATLAB for Machine Learning (3:13)

Meet Your Instructor (1:24)

Course Outlines (1:43)

MATLAB Crash Course

MATLAB Pricing and Online Resources

MATLAB GUI (4:57)

Some common Operations (11:56)

Grabbing and Importing a Dataset

Data Types that We May Encounter (6:02)

Grabbing a dataset (2:20)

Importing Data into MATLAB (9:35)

Understanding the Table Data Type (11:36)

K-Nearest Neighbor

Nearest Neighbor Intuition (9:19)

Nearest Neighbor in MATLAB (9:39)

Learning KNN model with features subset and with non-numeric data (10:48)

Dealing with scalling issue and copying a learned model (3:32)

Types of Properties (11:22)

Building a model with subset of classes, missing values and instances weights (6:58)

Properties of KNN (5:08)

Naive Bayes

Intuition of Naive Bayesain Classification (15:43)

Naive Bayes in MATLAB (10:34)

Building a model with categorical data (6:24)

A Final note on Naive Bayesain Model (3:00)

Decision Trees

Intuition of Decision Trees (9:01)

Decision Trees in MATLAB (5:35)

Properties of the Decision Trees (14:24)

Node Related Properties of Decision Trees (9:20)

Properties at the Classifer Built Time (7:25)

Discriminant Analysis

Intuition of Discriminant Analysis (6:44)

Discriminant Analysis in MATLAB (4:41)

Properties of the Discriminant Analysis Learned Model in MATLAB (7:03)

Machine Learning For Data Science Using MATLAB

Get a Feel for the Science Behind Siri & Other AI at the Beginner Level

Practical and hands-on, this beginner-friendly course covers clustering and classification algorithms, two machine learning essentials that help computers organize the data they receive. Whether it's Siri recognizing your voice or a marketing program identifying the best customers, these algorithms pave the way for many of today's AI breakthroughs, and you'll come to implement them both with MATLAB.

Access 57 lectures & 9.5 hours of content 24/7

Learn how to implement classification & clustering algorithms using MATLAB

Get a beginner-friendly introduction to coding w/ MATLAB

Develop real skills by learning from a malware analysis project

Instructor

Nouman Azam received his Ph.D. Degree in Computer Sceince from the University of Regina in 2014. Prior to that, he completed his M.Sc. in Computer Software Engineering from the National University of Sciences and Technology, Pakistan and earned his Bachelors in Computer Sciences from the National University of Computer and Emerging Sciences, Pakistan in 2007 and 2005 respectively

He is the creator of six online MATLAB courses. He has extensive knowledge of tools, such as MATLAB, QTSpim, C++, Java, LaTeX and other academic resources used for teaching and instructing purposes. Overall, he has over 10 years of teaching and relevant experience at undergraduate and graduate level.

Data Analysis With MATLAB For Excel Users

Import, Analyze & Share Your Data Analysis Results From Excel Files

Excel is a phenomenal data-crunching tool, but even this ubiquitous program has its limitations. In this course, you'll learn how to optimize MATLAB to overcome the shortcomings Excel often burdens tech professionals with. You'll focus on how to supplement the capabilities of Excel by having access to thousands of customized mathematical and advanced analysis functions, flexible visualization tools, and the ability to automate your analysis workflows—all available in MATLAB.

Nouman Azam received his Ph.D. Degree in Computer Sceince from the University of Regina in 2014. Prior to that, he completed his M.Sc. in Computer Software Engineering from the National University of Sciences and Technology, Pakistan and earned his Bachelors in Computer Sciences from the National University of Computer and Emerging Sciences, Pakistan in 2007 and 2005 respectively

He is the creator of six online MATLAB courses. He has extensive knowledge of tools, such as MATLAB, QTSpim, C++, Java, LaTeX and other academic resources used for teaching and instructing purposes. Overall, he has over 10 years of teaching and relevant experience at undergraduate and graduate level.

Important Details

Length of time users can access this course: lifetime

Access options: desktop and mobile

Certification of completion not included

Redemption deadline: redeem your code within 30 days of purchase

Experience level required: beginner

Requirements

Internet required

Students must install MATLAB on their computers

Course Outline

Instructor and Course Introduction

Instructor Introduction (1:32)

Course Outline (1:33)

Task in Data Analysis (2:38)

Introduction to MATLAB

MATLAB introduction (Part 1) (1:58)

MATLAB Introduction (Part 2) (3:43)

Data Preprocessing and Importing from Excel

Column and row selection (7:06)

Preprocessing Data (3:56)

Preprocessing Data: finding unique elements and rows (11:00)

Preprocessing Data : Using the membership and equality operations (5:55)

Model A Car & Design A PID Controller In MATLAB + Simulink

Design Your Own Cruise Control System for a Tesla Model S

From cars to aircraft and even interplanetary rockets, control systems are everywhere; and they're what allow complicated machines to do precisely what we need them to with astounding precision. Using Simulink and MATLAB, this course will show you how to simulate a Tesla Model S P85 and design your very own cruise control system—an impressive feat for students, hobbyists, and engineers looking to sharpen their skills.

Access 11 lectures & 2.5 hours of training 24/7

Learn how to design your own cruise control system for a Tesla Model S

Understand & harness the physics behind any electric car

Use Simulink to establish the mathematical model of an electric DC motor

Eliott Wertheimer has always been impressed and passionate about flying machines and the ultimate frontier that space represents. This led him to graduate with a Masters in Aerospace Engineering as one of the top students at a leading UK university. Throughout this degree he was offered the opportunity to understand and apply advanced engineering concepts to different design projects.

In his final year, he consequently designed a proof of concept nuclear battery or Radioisotope Thermoelectric Generator (refer to his courses to learn more about these) for nanosatellites which was judged by academics as one of the best projects of his department and presented at the 4th Interplanetary Cubesat Workshop. Similarly, he developed, with a team of colleagues, an unmanned rotorcraft able to fight fires, carry cargo and surveil missions, which eventually won a design competition for Agusta Westland.

He is very excited to be able to share his knowledge with curious individuals, who, like him, want to know more about the engineering behind the wonderful machines that populate the sky.

The MATLAB & Simulink Bible: Zero To Hero

Get Simulink-Savvy by Making 10 of Your Own Modeling Projects

Simulink is an add-on product to MATLAB that allows users to rapidly create virtual prototypes and models—handy for testing out new ideas and concepts on the fly when you're designing a product. This course covers the basics of Simulink and will show you how to create models and run simulations of physical systems with real, project-based approaches. Follow along as you build 10 Simulink projects with the instructor, and you'll have complete access to all of the Simulink models and slides to reference whenever you need.

Ryan Ahmed is a best-selling online instructor who is passionate about education and technology. Ryan's mission is to make quality education accessible and affordable to everyone. Ryan holds a Ph.D. degree in Engineering from McMaster* University, with focus on Mechatronics and Electric Vehicle (EV) control. He also received a Master’s of Applied Science degree from McMaster, with focus on Artificial Intelligence (AI) and an MBA in Finance from the DeGroote School of Business.

Ryan held several engineering positions at Fortune 100 companies globally. Most recently, he worked as a Systems Engineering Lead at Samsung America and as a Senior Scientific Research and Experimental Development Technical Specialist at Fiat-Chrysler Automobiles (FCA) Canada. Ryan has taught several courses on Engineering, Science, Technology and Mathematics to over 10,000+ students globally. He is the recipient of the best paper award at the IEEE Transportation Electrification Conference and Expo (iTEC 2012) in Detroit, MI, USA.

Ryan is a Stanford Certified Project Manager (SCPM), certified Professional Engineer (P.Eng.) in Ontario, a member of the Society of Automotive Engineers (SAE), and a member of the Institute of Electrical and Electronics Engineers (IEEE). He is also the program Co-Chair at the 2017 IEEE Transportation and Electrification Conference (iTEC’17) in Chicago, IL, USA.