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The Essential MATLAB & Simulink Certification Training Bundle

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6.5 hours
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50

Machine Learning Classification Algorithms Using MATLAB

Take a Deeper Look at How Machines Classify Information

By Nouman Azam | in Online Courses

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.

  • Access 50 lectures & 6.5 hours of content 24/7
  • Explore the MATLAB basics & the Statistic and Machine Learning toolbox
  • Familiarize yourself w/ key classification algorithms, like K-Nearest Neighbor & Decision Trees
  • 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)
  • Support Vector Machines
    • Intuition of SVM Classification (7:41)
    • SVM in MATLAB (12:34)
    • Properties of SVM learned model in MATLAB (12:46)
  • Error Correcting Output Codes
    • Intuition of ECOC (5:29)
    • ECOC in Matlab (9:15)
    • ECOC name, value arguemnts (12:59)
    • Properties of ECOC model (4:51)
  • Classification with Ensembles
    • Ensembles in MATLAB (12:33)
    • Properties of Ensembles (5:28)
  • Validation Methods
    • Cross validition options (Part 1) (10:07)
    • Cross validition options (Part 2) (10:08)
  • Performance Evaluation
    • Making Predictions with the Models (8:06)
    • Determining the classification loss (7:59)
    • Classification Margins and Edge (15:23)
    • Classification Loss, Margins, Predictions and Edge for cross validated models (10:49)
    • Comparing two classifiers with holdout (13:16)
    • Computing Confusion Matrix (7:38)
    • Generating ROC Curve (9:45)
    • Generating ROC Curve based on the testing data (8:45)
    • More Customization and information while generating ROC (6:25)
    • Computing Accuracy, Error Rate, Specificity and Sensitivity (5:10)

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Lessons
57

Machine Learning For Data Science Using MATLAB

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

By Nouman Azam | in Online Courses

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.

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
  • MATLAB 2017a or later
  • No prior knowledge of MATLAB is required

Course Outline

  • First Section
    • 1 - Introduction to course (5:10)
    • 2 - Introduction to matlab (8:26)
  • --------------------------- Data Preprocessing ---------------------------
    • Code and Data
    • Section Introduction (1:54)
    • Importing the data into MATLAB (7:25)
    • Handling Missing Data (Part 1) (7:43)
    • Handling Missing Data (Part 2) (6:46)
    • Feature scaling (9:50)
    • Outliers (Part 1) (9:07)
    • Outliers (Part 2) (6:02)
    • Dealing with Categorical Data (Part 1) (9:50)
    • Dealing with Categorical Data (Part 2) (6:20)
    • Your Data Preproprocessing Timplate (3:58)
  • --------------------------- Classification ---------------------------
    • Code and Data
  • K-Nearest Neighbor
    • KNN Intuition (7:27)
    • KNN in matlab (Part 1) (10:13)
    • KNN in MATLAB (Part 2) (12:38)
    • Visualizing the Decision Boundaries of KNN (13:06)
    • Explaining the code of visualization (9:53)
    • Here is our classification template (4:21)
    • Customization options (part 1) (7:19)
    • Customization options (part 2) (10:32)
  • Naive Bayes
    • Intuition of Naive Bayesain (Part 1) (11:24)
    • Intuition of Naive Bayesain (Part 2) (15:00)
    • Naive Bayesain in Matlab (6:06)
    • Customization Options of Naive Bayesain In MATLAB (4:18)
  • Decision Trees
    • Decision Trees Intuition (10:24)
    • Decision tree in matlab (4:48)
    • Visualizing the decision tree using the view function (9:02)
    • Customization Options for Decision Trees (9:20)
  • Support Vector Machines
    • SVM Intuition (Part 1) (15:21)
    • Kernel SVM Intuition (6:45)
    • SVM in MATLAB (6:37)
    • Customization Options for SVM (9:30)
  • Discriminant Analysis
    • Discriminant Analysis Intuition (13:12)
    • Discriminant Analysis in MATLAB (4:01)
    • Customization Options for Discriminant Analysis (5:03)
  • Ensembles
    • Ensembles Intuition (14:15)
    • Ensembles in matlab (8:53)
    • Customization Options for Ensembles (13:02)
  • Performance Evaluation
    • Confusion Matrix (15:51)
    • Validation_methods (12:04)
    • Validation methods (Part 1) (12:08)
    • Validation methods (Part2) (8:32)
    • Evaluation (8:22)
  • -------------------------- Clustering ---------------------------
    • Code and Data
  • K-Means
    • K-Means Clustering Intuition (12:04)
    • Choosing the number of clusters (14:19)
    • K-means clustering in MATLAB (Part 1) (12:55)
    • K-means clustering in MATLAB (Part 2) (16:27)
  • Hierarchical Clustering
    • Hierarchical Clustering Intuition (Part 1) (9:41)
    • Hierarchical Clustering Intuition (Part 2) (15:38)
    • HC in matlab (19:25)
  • -------------------------- Dimensionality Reduction ------------------
    • Code and Data
    • PCA Intuition (7:40)
    • PCA in MATLAB (Part 1) (13:41)
    • PCA in MATLAB (Part 2) (17:00)
  • Project: Malware Analysis
    • Project Discription (8:17)
    • Customizing code templates for completing Task 1 and 2 (Part 1) (9:40)
    • Customizing code templates for completing Task 1 and 2 (Part 2) (5:30)
    • Customizing code templates for completing Task 3, 4 and 5 (17:59)
    • Project Code and Data

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2 hours
Lessons
25

Data Analysis With MATLAB For Excel Users

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

By Nouman Azam | in Online Courses

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.

  • Access 25 lectures & 2 hours of content 24/7
  • Access & import data from Excel files
  • Learn how to import, preprocess, analyze, visualize & generate data analysis reports
  • Customize the visualization of data
  • Learn statistical models that fit w/ data
  • Generate reports for sharing 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
  • 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)
    • Preprocessing Data Using using the Set Operations (5:21)
    • Importing data from excel to matlab (2:50)
    • Importing different types of data (6:19)
  • Data Analysis
    • Visualization of data (Part 1) (2:35)
    • Visualization of data (Part 2) (6:49)
    • Summary so Far (0:40)
    • Data Analysis with Curve Fitting App (7:47)
    • Automating the analysis of data (3:21)
    • Writing your own functions for quick processing (9:20)
  • Sharing Your Results
    • Generating reports for sharing purposes (4:29)
    • Useful options for generating reports (2:57)
    • Sharing your results with live script (7:17)
  • Using MATLAB from Excel Enviroment
    • Spread Sheet link (Introduction and installation) (5:07)
    • Advantages of Spread Sheet link (2:22)
    • Passing data between excel and MATLAB (4:49)
    • Calling MATLAB functions from Excel (4:08)

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2.5 hours
Lessons
11

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

Design Your Own Cruise Control System for a Tesla Model S

By FuroSystems | in Online Courses

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
  • Implement an engineering model in Simulink using blocks, transfer functions & MATLAB functions

Instructor

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.

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
  • Basic understanding of physics (force, velocity, acceleration, etc)
  • High school diploma for mathematics (differentiations etc)

Course Outline

  • The Mathematical Model
    • Design Brief and Objectives (8:38)
    • Battery Performance and Model Input (11:35)
    • Tesla Model S P85 Brushed DC Motor Equivalent (16:10)
    • The Forces at Play (10:17)
    • The Car's Plant Dynamics (10:26)
  • Simulink Model Implementation
    • Model Setup and Motor Transfer Function (18:22)
    • Complete car Dynamics Open Loop Model (11:27)
    • Testing the Open Loop Model (22:09)
    • PID Control Implementation (6:53)
    • Tuning and Testing the Complete Closed Loop Model (35:17)
    • In-Depth Analysis and Derivative Gain (15:24)

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Content
4.5 hours
Lessons
40

The MATLAB & Simulink Bible: Zero To Hero

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

By Ryan Ahmed | in Online Courses

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.

  • Access 40 lectures & 4.5 hours of content 24/7
  • Walk through the Simulink basics & get real, project-based training
  • Follow along & create 10 Simulink projects, like a battery model & PID controller
  • Enjoy complete access to all of the course's Simulink models & slides

Instructor

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.

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

Course Outline

  • PROJECT #1: GENERATE, DISPLAY AND EXPORT SOURCE GENERATING SINE WAVE
    • 1.1 Project Introduction (0:59)
    • 1.2 Sine wave simulation (17:22)
    • 1.3 Quiz
    • 1.4 Quiz Solution (3:31)
  • PROJECT #2: BUILD A MATHEMATICAL EQUATION (DIFFERENTIATION/INTEGRATION) SYSTEM
    • 2.1 Project Introduction (0:31)
    • 2.2 Equation simulations Simulink (5:43)
    • 2.4 Quiz Solution (2:19)
    • 2.5 Build equation Simulink (15:35)
  • PROJECT #3: SIMULATE A MASS SPRING DAMPER SYSTEM IN TIME DOMAIN
    • 3.1 Introduction (0:38)
    • 3.2 Mass Spring damper intro (5:56)
    • 3.3 Mass spring damper simulation (13:08)
    • 3.5 Quiz Solution (7:20)
  • PROJECT #4: SIMULATE A MASS SPRING DAMPER SYSTEM IN S-DOMAIN USING SIMULINK
    • 4.1 Project Intro (0:38)
    • 4.2 and 4.3 Mass Spring Damper S Domain (8:38)
    • 4.4 Mass Spring in S Domain Simulation (9:31)
    • 4.5 Quiz Solution (5:39)
  • PROJECT #5: BUILD AND SIMULATE A BATTERY MODEL
    • 5.1 Project Introduction (1:04)
    • 5.2 and 5.3 Battery Charging (8:59)
    • 5.4 Simple Battery Model (9:21)
    • 5.5 Simple battery model simulink (27:51)
    • 5.7 Quiz Simple Battery Model (4:56)
  • PROJECT #6: BUILD PROPORTIONAL INTEGRAL DERIVITIVE (PID) CONTROLLER IN SIMULINK
    • 6.1 Introduction to PID Project (1:24)
    • 6.2 Control System introduction (10:27)
    • 6.3 Steps to Develop Control Systems (3:12)
    • 6.4 PID Controller (10:01)
    • 6.5 PID Controller in Simulink 2 (6:41)
  • PROJECT #7: APPLY A PID CONTROLLER TO MASS SPRING DAMPER SYSTEM
    • 7.1 PID with Mass Spring Damper Intro (0:47)
    • 7.2 PID with Mass Spring Damper Simulation (10:41)
  • PROJECT #8: TUNE A PROPORTIONAL INTEGRAL DERIVITIVE (PID) CONTROLLER
    • 8.1 Project Introduction (0:38)
    • 8.2 PID Tuning Using PID Block (8:45)
    • 8.3 PID Tuning in Simulink (13:07)
  • PROJECT #9: DEVELOP AND SIMULATE ADAPTIVE CRUISE CONTROL SYSTEM
    • 9.1 Project Intro Block Reduction (1:02)
    • 9.2 Cruise Control Model (5:21)
    • 9.3 Integrate model with Controller (3:52)
    • 9.5 Quiz Solution Model Reduction (3:51)
    • 9.6 Cruise Control Simulation in Simulink (11:12)
  • PROJECT #10: DC MOTOR POSITION CONTROL IN SIMULINK
    • 10.1 Introduction (0:34)
    • 10.2 DC Motor Theory of Operation (6:41)
    • 10.3 DC Motor Model (8:56)
    • 10.4 DC Motor Simulation in Simulink (12:25)
    • 10.6 Quiz PID Controller with DC Motor (4:24)

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  • Unredeemed licenses can be returned for store credit within 15 days of purchase. Once your license is redeemed, all sales are final.