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The Complete MATLAB Programming Master Class Bundle

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Content
3 hours
Lessons
31

Advanced MATLAB Data Types & Data Structures

Learn Cells, Tables, Structures, & Other Essential Data Types

By Nouman Azam | in Online Courses

Description

MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creating of user interfaces, and interfacing with programs written in other languages. That's all well and good, but it means nothing if you don't have a firm grasp of the data types used within MATLAB. In this course you'll cover not just data types, but also dive into their functions and how to perform conversions to make analysis and programming a greater experience.

  • Access 31 lectures & 3 hours of content 24/7
  • Learn the essential, unique MATLAB data types necessary for MATLAB programming & data analysis
  • Use different data types & structures such as cells, tables, time tables, structures, & map containers
  • Convert between different data types

Instructor

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

Nouman has over 10 years of teaching experience. He has taught almost all the major computer science subjects including introduction to computers, computer organization and architecture, operation systems, computer networks, image processing, digital logic design, discrete structures and many others. He has extensive knowledge of tools such as MATLAB, QTSpim, C++, Java and Other academic tools used for teaching and instructing purposes.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: advanced

Requirements

  • Internet required

Course Outline

  • Introduction to the course
    • Course Outlines - 2:24
    • Instructor Introduction - 1:32
  • Cell Data Type
    • Defining cells - 6:31
    • Access Data in a Cells - 7:55
    • Adding and deleting elements from a cell - 5:35
    • Concatenating Cells and Passing Cell Contents to a Function - 6:08
  • Table Data Type
    • Defining Tables - 8:23
    • Adding descriptions, Units and accessing individual variables - 8:00
    • Selecting and reordering rows and columns - 5:08
    • Sorting rows of a table - 3:58
    • More properties of the table - 2:06
    • reading and writing tables - 9:55
    • Adding and deleting rows from a table - 6:48
    • adding and deleting columns - 2:30
    • Storing summary of a table - 4:25
  • Time Tables Data Type
    • Time table data structure - 10:18
    • Properties, Sorting, and data selection in time tables - 7:45
    • Concatenating timetables - 8:28
    • Indexing and retrieving data based on row time - 2:39
  • Structures Data Types
    • Creating Structures - 6:51
    • Retrieving data from a field of a structure - 4:39
    • Concatenating structures - 5:11
    • Storing data of a field in variable - 5:31
    • More operations on structures - 4:09
  • Map Containers Data Types
    • Creating Map Containers - 6:03
    • More operations on containers and concentation - 8:12
  • Data Type Conversions
    • Introduction to the segment - 1:36
    • From other data types to cell - 12:09
    • Cell to other data types - 11:42
    • Converting other data types to table - 4:13
    • Converting from and to table data type - 5:48

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11

Simulate an Electric Car & Design a Cruise PID Controller

Simulate a Tesla Model S P85 & Design Your Own Cruise Control System in MATLAB & Simulink

By Eliott Wertheimer | in Online Courses

Description

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 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: web streaming, mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: intermediate

Requirements

  • Internet required

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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4 hours
Lessons
30

Data Preprocessing for Machine Learning Using MATLAB

Implement Commonly Used Data Preprocessing Techniques in MATLAB with Practical Examples, Projects & Datasets

By Nouman Azam | in Online Courses

Description

If you want to fully equip yourself with the art of applied machine learning using MATLAB, this course is for you. You'll start with a practical tutorial of MATLAB and scale up slowly, learning how to apply data preprocessing techniques without performing complicated math. By the end of this course, you'll know the most commonly used data preprocessing techniques that you can use to instantly maximize your insight into data sets.

  • Access 30 lectures & 4 hours of content 24/7
  • Get an introduction to MATLAB
  • Learn how to handle missing values
  • Deal w/ categorical variables
  • Understand how to detect outliers
  • Discuss feature scaling & data discretization

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: web streaming, mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: intermediate

Requirements

  • Internet required

Course Outline

  • Introduction to course and MATLAB
    • 1 - Intorduction to Course - 4:24
    • 2 - Introduction to matlab - 8:26
    • 3 - Importing the dataset into MATLAB - 7:34
  • Handling Missing Values
    • Code and Data
    • 1 - Deletion Strategies - 8:41
    • 2 - Using mean and mode - 10:42
    • 3 - AddingSpecialValue - 6:58
    • 4 - classspecificmode_mean - 12:48
    • 5 - RandomValueImputation - 14:05
  • Dealing with Categorical Variables
    • Code and Data
    • 2 - Categorical data with order - 6:11
    • 1 - Categorical data with no order - 9:51
    • 3 - Frequency_encoding - 13:04
    • 4 - TargetbasedEncoding - 9:20
  • Outlier Detection
    • Code and Data
    • 1 - 3 sigma rule with deletion strategy - 11:27
    • 2 - 3 sigma rule with filling strategy - 5:55
    • 3 - Histograms for outliers - 12:56
    • 4 - Box Plots (Part 1) - 8:18
    • 5 - Box Plots (Part 2 - 15:41
    • 6 - LOF (Part 1) - 6:21
    • 7 - LOF (Part 2) - 12:49
    • 8 - Outliers in categorical variables - 8:03
  • Feature Scaling and Data Discretization
    • Code and Data
    • 1 - Feature Scalling - 8:50
    • 2 - Equal Width Binning - 15:48
    • 3 - Equal Frequency Binning - 7:35
  • Project: Selecting the Right Method for your Data
    • Code and Data
    • Selecting the right method (Part 1) - 16:53
    • Selecting the right method (Part 2) - 10:59

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2 hours
Lessons
40

Create Apps in MATLAB Using GUIDE

Make Your Code More Useable & Accessible By Users

By Nouman Azam | in Online Courses

Description

This basic course will teach you how to create graphical user interfaces in MATLAB using the GUIDE utility. Through this course, you'll transform your code into an attractive piece of software that users can actually interact with. By course's end, you'll be able to not just write MATLAB code, but make it far more presentable and useable.

  • Access 40 lectures & 2 hours of content 24/7
  • Become a confident user of GUIDE
  • Work w/ graphical user interface controls like text boxes, buttons, check boxes, & more
  • Understand how to link code w/ the GUI

Instructor

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

Nouman has over 10 years of teaching experience. He has taught almost all the major computer science subjects including introduction to computers, computer organization and architecture, operation systems, computer networks, image processing, digital logic design, discrete structures and many others. He has extensive knowledge of tools such as MATLAB, QTSpim, C++, Java and Other academic tools used for teaching and instructing purposes.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: advanced

Requirements

  • Internet required

Course Outline

  • Introductory Notes and Remarks
    • Instructor Introduction - 1:32
    • Course Outlines - 1:29
    • Why take this course - 1:13
    • learning style and objectives - 0:56
  • Basics of the Guide
    • Accessing and Opening Guide - 0:39
    • Available controls with their types and the generated files Preview - 4:32
    • Properties of controls (Initial values and tags). - 4:45
    • Positioning and aligning controls. - 2:06
    • Grid and Lines - 2:49
    • Customizing Tabbing Behaviour - 3:42
    • The created functions in the .m file - 3:50
    • The set and get functions - 3:00
  • Linking the code with the GUI
    • GUI for a simple product program Preview - 4:51
    • Including tables in GUI - 7:58
    • Working with the slider and including graphs - 4:38
    • Setting up a background image of a button - 1:28
    • Setting the menu. - 5:16
    • Changing the backgrounds of a GUI - 5:22
    • Button group and radio buttons - 5:33
    • Reading a file (text file) and displaying its contents - 4:47
    • Using checkboxes - 4:46
    • Explaining toggle buttons - 2:47
    • hObject and Handles - 1:35
    • pop up menu and list boxes - 3:55
  • Advance Techniques for GUIDE
    • Passing Values between GUI's - 3:31
    • Passing Values Between Two Call Back Functions - 6:25
    • How to pass command line arguments to GUI - 2:19
    • Useful Resources
  • Sample Projects with GUIDE
    • Building a Calculator (Part 1) - 16:11
    • Building a Calculator (Part 2) - 4:44
    • Image processing Project (Part 1) - 7:53
    • Image processing Project (Part 2) - 8:35
  • More Useful Tricks and Examples with GUIDE
    • A trick with the visibility option of text box_f - 3:30
    • Simple string manipulation and user notification - 3:57
    • Deleting elements from a List box one by one programmatically - 5:03
    • Selectiong Determination and Counter - 4:42
    • User notifications during processing with a push button - 4:19
    • Interacting with GUI from KeyBoard - 2:33
    • List box choice restriction - 4:42
    • adding elements to a list box - 2:43

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Lessons
40

Create Apps in MATLAB Using App Designer

Develop Apps with MATLAB Like a Pro

By Nouman Azam | in Online Courses

Description

The App Designer utility in MATLAB contains many new design components absent in GUIDE, and allows you to take your GUI-creating skills to a whole new level. Through this course you'll learn how to optimize App Designer to transform your code into interactive software in more advanced and technical ways.

  • Access 40 lectures & 2 hours of content 24/7
  • Work w/ graphical user interface controls like text boxes, buttons, check boxes, & more
  • Become a confident user of App Designer to create great GUI for your programs
  • Create your own GUIs from scratch

Instructor

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

Nouman has over 10 years of teaching experience. He has taught almost all the major computer science subjects including introduction to computers, computer organization and architecture, operation systems, computer networks, image processing, digital logic design, discrete structures and many others. He has extensive knowledge of tools such as MATLAB, QTSpim, C++, Java and Other academic tools used for teaching and instructing purposes.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: advanced

Requirements

  • Internet required

Course Outline

  • Introductory Notes and Remarks
    • Instructor Introduction - 1:32
    • Course Outlines - 1:16
    • Learning style and objective - 0:56
    • A few notes before starting the course - 1:31
  • Basics of App Designer
    • The design view and code view - 4:58
    • Briefing on available controls in App Designer - 4:41
    • Alignment and arranging options - 3:53
    • Spacing and resizing - 3:18
    • Grid lines - 2:33
  • tips and Tricks for Effective Use of App Designer
    • Error detection and correction mechanism of app designer - 3:32
    • useful shortcuts-1 - 2:23
    • Useful Shortcuts-2 - 4:18
    • Dragging Components with Ctril key - 1:15
  • Coding GUI's
    • Important notions before stating to code (Part 1) - 4:21
    • Important notions before starting to code (Part 2) - 4:27
    • Simple addition program - 7:02
    • slider and graph - 7:35
    • label and text area - 2:42
    • list boxes - 2:56
    • Drop Down Menu - 3:22
    • Rradio buttons - 7:27
    • state button and spinner - 4:14
    • Working with different types of switches - 5:04
    • Opening a text file and displaying its contents - 3:35
    • Working with Tables - 7:51
    • Guages- a speedometer example - 3:25
    • Knobs and Discrete Knobs - 4:12
    • lamps - 2:36
    • Working of Tab Group - 5:13
  • Advance Techniques
    • passing values between callback functions - 4:03
    • passing values between two different GUIs - 6:01
    • Adding a custom built public function - 3:15
    • Adding custom private functions in the GUI's - 5:30
    • Including background images - 2:49
    • Calling multiple apps from script - 3:36
    • Packaging your app - 4:13
  • Sample Projects
    • Building a calculator (Part 1) - 7:02
    • Building a calculator (Part 2) - 6:42
    • Building Image Processing App (Part 2) - 7:34
    • Building Image Processing App (Part 1) - 5:21

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6 hours
Lessons
79

MATLAB Programming & Problem Solving: Go from Beginner to Pro

Cover the General Essentials of Working with MATLAB

By Nouman Azam | in Online Courses

Description

MATLAB (Matrix Laboratory) is a multi-paradigm numerical computing environment and programming language that is frequently used by engineering and science students. In this course, you will be introduced to MATLAB at a beginner level, and will gradually move into more advanced topics. The key benefit of MATLAB is how it makes programming accessible to everyone, allowing you to resolve complex problems with less complex code.

  • Access 79 lectures & 6 hours of content 24/7
  • Use MATLAB confidently to solve problems
  • Run scripts, write code, & do data analysis & visualization
  • Solve equations, do math operations, & manipulate matrices
  • Reinforce your understanding w/ added practice questions & solutions
  • Formulate your own logic & convert complex problems into MATLAB code & solve them using programming skills

Instructor

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

Nouman has over 10 years of teaching experience. He has taught almost all the major computer science subjects including introduction to computers, computer organization and architecture, operation systems, computer networks, image processing, digital logic design, discrete structures and many others. He has extensive knowledge of tools such as MATLAB, QTSpim, C++, Java and Other academic tools used for teaching and instructing purposes.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: web streaming, mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Requirements

  • Internet required

Course Outline

  • Course and Instructor Introduction
    • Introduction to Course - 3:13
    • Introduction to Matlab Software - 5:01
    • MATLAB Graphical User Interface - 5:38
    • Some common Operations - 5:57
    • how to connet with me
  • Handling Variables and Creating Scripts
    • Lets lay foundations for understanding variables - 6:31
    • Creating scripts and understanding commenting and semicolon effect - 6:29
    • Different types of variables (Strings, characters and logical) - 8:10
  • Doing Basic Math in MATLAB
    • Basic Maths (addition, multiplication, subtraction and powers) - 7:30
    • Understanding operation precedence - 6:02
    • Additional Math Functions (GCD, LCM, Prod, PERMS, Prime) - 6:11
    • Trignometric functions - 5:15
    • Set operations - 5:21
    • Computing statistics of the matrices - 10:00
    • Handling Random Numbers - 4:43
    • Cross and dot product - 4:24
    • Basic logical operation (And, Or and Not) - 4:16
    • Sign and absolute functions - 4:23
    • Converting numbers between different bases - 7:23
    • Discretizing your data - 9:13
    • Practice Exercise Question
    • Solutions to Practice Exercise Question
  • Operations on Matrices
    • Determining unique elements - 11:00
    • Determining membership of elements to a matrix - 5:55
    • Shifting matrix elements - 4:39
    • Determinant, inverse and diagnal elements - 2:37
    • The colon operator for data selection - 4:14
    • Relational operations - 7:12
    • Some commonly used matrices - 3:15
    • Sorting matrix values - 6:01
    • Size and length functions - 3:14
    • Concatenating matrices - 2:15
    • Finding non-zero elements - 4:44
    • Frequencies of values within a vector - 5:17
    • Practice Exercise Questions (Beginner)
    • Solutions to Practice Exercise Question (Beginner)
    • Practice Exercise Questions (Advance)
    • Solutions to Practice Exercise Question (Advance)
  • Advance Math Functions with Symbolic Data Type
    • Symbolic variables - 5:27
    • Differentiation and integration using symbolic variables - 5:20
    • Solving equations - 8:36
    • Symbolic functions - 2:20
  • Interacting with MATLAB and Graphics
    • Basic Input Output Commands - 5:01
    • More Input/output Options - 5:20
    • Plotting data - 7:19
    • Ploting 3-D data - 3:00
    • More plotting options - 8:10
    • Combining plots with hold on - 4:03
    • Interacting with the plot using the brush tool - 6:11
    • Creating plots with two y-axis - 4:02
    • Animated line - 3:05
    • Bar graphs - 8:20
    • Checking for existence of files, scripts, folders, functions or class - 3:47
    • Manipulating Directory (Part 1) - 7:13
    • Manipulating Directory (Part 2) - 7:21
    • Processing a text file - 12:21
    • Project
    • Project Solution
  • Importing Data into MATLAB
    • Importing data from excel to matlab - 2:50
    • Importing different types of data - 6:19
    • Practice Exercise Questions
    • Solutions to Practice Exercise Questions
  • MATLAB Programming
    • Conditional If statement (Part 1) - 4:14
    • Conditional If statement (Part 2) - 6:18
    • For loops for iterating through your code - 8:10
    • Nested for loops - 7:44
    • While loops (when you don't know the number of iterations) - 8:29
    • Breaking out from a loop before final condition - 4:54
    • Continue statement for skipping an iteration - 6:03
    • Switch statements for choice selection - 6:17
    • Practice Exercise Question
    • Solutions to Practice Exercise Questions
    • Practice Exercise Questions (Challenge)
  • Making Your Own Functions
    • Creating custom build functions - 3:03
    • Functions with inputs - 4:09
    • Functions with multiple inputs and outputs - 5:44
    • The return statement inside a function - 3:49
  • Sharing Your Results
    • Generating reports with the publishing options - 5:16
    • Sharing your results with live script - 7:17

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Lessons
51

Machine Learning Classification Algorithms Using MATLAB

Take a Deeper Look at How Machines Classify Information

By Nouman Azam | in Online Courses

Description

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 51 lectures & 6 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: web streaming, mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Requirements

  • Internet required

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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9 hours
Lessons
62

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

Description

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 62 lectures & 9 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: web streaming, mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: beginner

Requirements

  • Internet 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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Lifetime
Content
1 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

Description

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 & 1 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: web streaming, mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Requirements

  • Internet required

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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Lifetime
Content
4 hours
Lessons
49

MATLAB/Simulink Bible

Build 10 Practical Projects & Go from Beginner to Pro with This Project-Based Simulink Course

By Ryan Ahmed | in Online Courses

Description

This best-selling course will cover the basics of Simulink, showing you how to create Simulink models and run simulations of physical systems. The course includes a unique project-based learning approach and you are going to learn by doing

  • Access 49 lectures & 4 hours of content 24/7
  • Experience a true practical project-based learning experience by building 10 Simulink projects
  • Access all the 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: web streaming, mobile streaming
  • Certification of completion included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Requirements

  • Internet required

Course Outline

  • Course Introduction and Welcome Message
    • 0.0 Simulink Index Lecture - 1:11
    • Course Materials
  • 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
    • 2.3 Quiz
    • 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
    • 3.4 Quiz
    • 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
    • 4.6 Quiz
    • 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
    • 5.6 Quiz
    • 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
    • 9.4 Quiz
    • 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
    • 10.5 Quiz
    • 10.6 Quiz PID Controller with DC Motor - 4:24

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Terms

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