MATLAB for beginners 10 easy steps 2019

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matlab for beginners 10 steps 2019
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Top Tutorials To Learn MATLAB For Beginners 10 easy steps 2019

As the volume and complexity of data and results continue to grow with the increasing complexity of data sources and algorithms, the need for intuitive representations of that data and results becomes increasingly critical.

We want to create the representations in such a way that the human mind can, after all, better understanding our universe and the processes taking place within — representation of a real-world object, an abstract mathematical expression, specific values of some measurable quantities, etc. Since 80 percent of the sensory information the brain receives comes from our eyes, the visual presentation of data is a natural choice.

The graphical representation of the results is often not only the most effective means of conveying the points of the study or work which has provided the data but is in most cases an expectation of the audience of the work. It helps you to identify and emphasize areas of interest in data behavior, to express your thoughts, observations, and conclusions to others in a quick and intuitive way.

Why MATLAB?

  • Ease of Use

MATLAB is an interpreted language. Programs may be easily written and modified with the built-in integrated development environment and debugger.

  • Platform Independence

MATLAB is supported on many different computer systems, providing a large measure of platform independence. The language is supported on Windows, Linux, Unix, Macintosh. Programs written on any platform will run on all of the other platforms.

  • Device-Independent Plotting

MATLAB, unlike other computer languages, has many integral plotting and imaging commands. The plots and images can be displayed on any graphical output device supported by the computer on which MATLAB is running. This capability makes MATLAB an outstanding tool for visualizing data.

  • Full set capabilities

MATLAB has all graphics functions necessary to visualize scientific and engineering data. It includes features for representation of two-dimensional and three-dimensional diagrams, three-dimensional volume visualization, animation, tools to create diagrams interactively and the possibility of exporting to the most popular graphics formats.

It is possible to customize diagrams adding multi-axis, change the colors of the lines and markers, add annotations, LaTeX expressions, legends, and other plotting options.

#1 Become a Good Matlab Programmer in less than 30 days

This is the last time; you wish you could be a Matlab Programmer.

Matlab Programming is one of the most important technical programming languages and skills today. In this course, we will start learning Matlab from a beginner level, and slowly we ease our way into more technical topics. This course is a general Matlab Programming, and it means that all the majors can benefit from this course.

Matlab Programming is an easy and understandable programming language and is an excellent choice for learning before starting other programs like Java, Python, C, and C++.

The list of contents is:

Chapter 1: An Introduction to Matlab Software

Chapter 2: Mathematics in Matlab

Chapter 3: Working with Variables in Matlab environment

Chapter 4: Trigonometric Functions in Matlab

Chapter 5: Complex Numbers in Matlab

Chapter 6: Working with Vectors in Matlab

Chapter 7: Working with Matrices in Matlab

Chapter 8: Introduction to Calculus and Engineering Functions in Matlab

Chapter 9: Graphs and Plotting in Matlab

Chapter 10: Loops, Conditions, and Intro to Programming in Matlab

Chapter 11: Projects (Updates Weekly with new programming drills)

Chapter 12: Import Data from Excel to Matlab

Chapter 13: How to Claim your Coursovie Training Certificate (LinkedIn)

Chapter 14: Bonus Materials for the Course

Chapter 15: Massive Discount Codes for other Courses (Coursovie Collection)

 #2 Complete MATLAB Tutorial: Go from Beginner to Pro

Essential MATLAB Tutorial that will take you from beginner to advance level.

MATLAB (matrix laboratory) is a multi-paradigm numerical computing environment and fourth-generation programming language which is frequently being used by engineering and science students. In this course, we will start learning MATLAB from a beginner level, and will gradually move into more technical and advanced topics.

This course is designed to be general in scope which means that it will be beneficial to students in any major. Once, passed a certain learning threshold, you will enjoy MATLAB Programming. The key benefit of MATLAB is that it makes the programming available to everyone and is very fast to turn ideas into working products compared to some of the conventional programming languages such as Java, C, C++, visual basic and others.

Below is the detailed outline of this course.

Segment 1: Instructor and Course Introduction

Segment 2: Handling variables and Creating Scripts

Segment 3: Doing Basic Maths in MATLAB

Segment 4: Operations on Matrices

Segment 5: Advanced Math Functions with Symbolic Data Type

Segment 6: Interacting with MATLAB and Graphics

Segment 7: Importing Data into MATLAB

Segment 8: File Handling and Text Processing

Segment 9: MATLAB Programming

Segment 10: Sharing Your MATLAB Results

Segment 11: Cell Data Type

Segment 12: Tables and Time Tables

Segment 13: Working with Structures and Map Container Data Type

Segment 14: Converting between Different Data Types

#3 Learn MATLAB and Simulink Programming

Master MATLAB Programming fundamentals and Simulink to increase your number crunching abilities.

Learn MATLAB and Simulink Programming is a course that focuses on teaching students about the various commands, functions, and features that MATLAB and Simulink have to offer. MATLAB and Simulink have a lot of capabilities, and so this course will only focus on the main topics to get you comfortable creating your own scripts and Simulink models.

This course contains many examples of different projects as well as step-by-step solutions to help you best understand what is going on. The completed code is attached in the projects so that students can download and get the same results they see onscreen.

This course is designed to teach students through a combination of articles to help explain various topics and videos to show examples of these topics. There are also various quizzes that are designed to test students and let them know if they sufficiently understand the information presented in the section.

This course starts out by briefly covering an overview of the MATLAB environment and where specific tools are located. Each section of this course covers different topics including the following:

  • Generating Figures
  • Plotting Data
  • Basic MATLAB Commands
  • Vectors
  • Matrices and Matrix Commands
  • MATLAB Scripts
  • Programmings Loops & Conditional Statements
  • User Defined Functions
  • Simulink
  • Simulink Features
  • Example Simulink Projects

There are several quizzes that will test your understanding of the various sections. There are multiple projects that require students to solve problems using MATLAB & Simulink.

Each of the projects in this course contains the following information:

Instructions: This article explains what is required to complete the project.

Demonstration: This lecture demonstrates what is expected of the students regarding how to complete the project.

Step-By-Step Solution: This lecture explains the thought process and how to complete the project in a step-by-step fashion.

#4 Learn MATLAB programming, debugging, and style

Learn the key MATLAB programming skills that separate experts from novices: debugging, functions, and visualization.

MATLAB is one of the most important and widely used programming environments, data visualization tools, and numerical solvers in academia and industry. On the other hand, MATLAB is just a programming language, not so different from learning other “high-level” coding or visualization languages.

Therefore, my goal in this course is not just to teach you how to code in MATLAB, it’s to teach you high-level, transferable skills that will help you become a better programmer, regardless of whether you are using MATLAB, Python, R, JavaScript, or any other language.

What you will learn in this course:

  • Using and customizing the visual MATLAB environment (including replacing those awful default black-on-white colors with something that suits your colorful personality!)
  • MATLAB programming basics
  • Control statements (for-loops, while, if-else, switch)
  • Make your own MATLAB functions
  • Create and edit data visualizations using the MATLAB graphics engine
  • How to write good, clean, readable code
  • General strategies for debugging (finding and fixing errors)
  • Recognize common coding mistakes and how to avoid them
  • How to organize and optimize your code before you start coding
  • Build confidence in your programming skills

#5 Data Visualization with MATLAB — Projects and Examples

Learn how to Visualize Data with MATLAB in 2D, 3D, 4D, 5D and create animated plots with tens of projects and examples.

Why this course?

  • Breaks the complex plot techniques down into simplistic steps.
  • Easy and intuitive approach from professional trainers.
  • Ideal for students, academics, scientists.
  • Suitable for beginner programmers.

matlab for beginners 10 steps 2019

#6 Learn Matlab Programming by Examples (Codes Included)

An Intermediate & advanced guide for getting proficiency in Matlab Programming Fast.

The list of contents is:

Chapter 1: General & Important Notes

Chapter 2: M-Files, Functions, and Scripts in Matlab

Chapter 3: Matrices and Vectors

Chapter 4: Importing and Exporting Files in Matlab

Chapter 5: Decision-Making Techniques in Matlab

Chapter 6: Strings & Text Manipulation

Chapter 7: Plotting & New Features

Chapter 8: Matlab Programming Techniques

Chapter 9: References and Conclusion

#7 MATLAB Basics for Beginners

Learn MATLAB Programming with step by step Exercises.

This is a practicing course for MATLAB. Learn the leading software in numerical computing through step by step exercises. Master the basics and move to an advanced level in MATLAB.

MATLAB is a leading software in numerical computing and building algorithms that are widely used by Engineers, Programmers, Researchers, Teachers, Colleges and Entrepreneurs.

In this course you will start learning MATLAB by creating and manipulating Matrices which are the key for MATLAB programming, then you will learn how to use MATLAB in some Elementary Mathematics Problems, after that comes the Graphics section in which you will learn how to use MATLAB to produce 2D & 3D graphs also how to build 2D animations.

In the programming section, you will learn how to use MATLAB as a programming language to build your own Algorithms, you will learn how to import and analyze data to MATLAB, and finally, you will get introduced to the symbolic capabilities of MATLAB.

#8 Learn MATLAB with Image Processing from scratch!

Learn how to use MATLAB with this awesome toolbox of Image Processing. No previous experience required at all.

MATLAB’s Image Processing (IP) toolbox is insanely popular and widely used in almost all academic Institutions and Enterprises. That’s because, it is so well written and organized, that it makes this toolbox very user-friendly for even the toughest of the IP operations.

If you want to learn MATLAB for your Work or College, this is the right course for you. This course teaches you this complete Image Processing toolbox from scratch. All the theoretical concepts have been explained in presentations with examples. Then, their implementation is covered with Matlab Programming. All these presentation files and working Matlab scripts are provided as supplementary materials along with the lectures. You don’t need any previous Matlab Programming experience to take this course, as it starts everything from scratch.

The course content covers all the (Beginner and Intermediate Level) topics in IP toolbox like Image Filtering, Noise Removal, Morphological Operations, Histogram operations, Image Thresholding, Edge Detection and basics of Image Segmentation. Several quizzes have been set up to keep track of your performance and understanding.

Besides all the theoretical content, some real-world applications have been covered as well in the form of Projects Like “Detect the faces of all your friends in an image.”

#9 MATLAB for Scientists and Engineers

Introduction to MATLAB for Engineers and Scientists.

Whether of engineering, science, economics or medical background, you are about to join over 2 million users of MATLAB that cut across these backgrounds; a multi-paradigm numerical computing environment and fourth-generation programming language that allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, C#, Java, Fortran and Python with additional package, Simulink, adds graphical multi-domain simulation and model-based design for dynamic and embedded systems.

This course starts from the elementary topics, then progressively and systematically advances to more advanced (but well explained) topics in MATLAB. It touches the major topics Engineers, and Scientists meet on daily bases and major aspects of MATLAB you need to progress to become an expert.

After this course, you can stand boldly and tackle those difficult problems on your own with MATLAB and be able to proceed and specialize on any aspect of MATLAB you choose to.

Each lecture has a quiz which must be attempted to obtain a certificate for this course. Each section has an assignment which can be evaluated by fellow Udemy students if you give the permission. Note that some of these quizzes serve as a summary of the course, you will get to learn some more things and also understand some key facts emphasized in the lectures. The step-by-step answers to the assignment are also provided.

In this course, you will typically become a guru and will move from zero knowledge in MATLAB to hero.

#10 MATLAB Projects with iPhone & iOS Sensors

Build your own MATLAB programs using the Accelerometer, Compass, GPS & other iOS Sensors on your iPhone & iPad.

Now you can collect sensor data from your iPhone or Apple iOS device using MATLAB! Impress your friends, build some cool programs, and take your MATLAB skills to a new level with this interactive course. You’ll learn how to capture and utilize data from 5 different sensors without the need to buy additional software or hardware.

Build 6 iPhone Sensor Projects (Source Code Included)

In this course, you will master the MATLAB® Support Package for Apple iOS Sensors by building a unique set of projects which enable you to:

  • Build Your Own 3D Compass
  • Rotate 3D Objects Using Your Phone
  • Detect iPhone Facing Up or Down
  • Track Position and Speed of A Car
  • Detect Shaking
  • Count Your Steps While Walking

Getting set up is a little tricky, but, don’t worry, I’ll walk you through it. And if you have any problems, you can ask for help through the discussion forum. I’ll show an easy method to enable and acquire data from all 5 iOS sensors including:

  • Acceleration Sensors — Learn how to use abrupt changes in your iPhone’s movement
  • Magnetic Field Sensors — Explore magnetic fields used by the compass and other input devices
  • Orientation Sensors — See how you can detect your iPhone state and control virtual items
  • Angular Velocity Sensors — Detect your iPhone’s movement and use to control software
  • Position Sensors — Capture your GPS position and speed and build location-based apps

You will learn how easy it is to enable these sensors and acquire data for your MATLAB programs

Also Read: Easy Way to Learn Python


Compreson Between Matlab and Python

Python codeMATLAB code
# numeric variables

# are double precision by default

 

a = 5.0

% numeric variables

% are double precision by default

 

a = 5.0;

# repeat which assigns values to array elements

# arrays are known as “lists” in Python
# array indexes start at 0 in Python

# structures are defined by indentation, no ‘end’
A = [] # initialize array A
for i in range(1,11):
A.append(i)
print(A[i-1])

% array indexes start at 1 in Matlab% indentation is for readability only
for i=1:10A(i) = i;end
A % display contents of A
# repeat which prints a series of

# values
for i in range(0,11,2):
print(i)

for i=0:2:10fprintf(‘ %i \n’, i)

end

# initialize an identity matrix

# import the numpy library for matrix operations

import numpy as np

B = np.identity(3)

% MATLAB has built-in functions for% common array initializations

 

B = eye(100);

# declare and initialize an array,
# known as a list in PythonC = [1, 2, 3]
C = [1, 2, 3];  % or C = [1 2 3];
# initialize and print an array
# array name = arange(start,stop,step)import numpy as np
C = np.arange(2,10,2)
print(C)
% array name = [start:increment:end];

C = [2:2:8] % leave off ; to display value

# print an array element on screen
# array indexes start at 0print(C[1])# prints 4 using C from above table cell
# note square brackets C[1]
% array indexes start at 1
C(2)% prints 4 using C from above table cell
% note parentheses C(2)
# declare and initialize an array

# with fixed interval between values

import numpy as np
C = np.linspace(2,8,4)

# third param is optional and = # points

# between and including 1st two points

# if third param left off, default

# is 50 points

 

 

C = linspace(2,8,4);

 

% third param is optional and = # points

% between and including 1st two points

% if third param left off, default

% is 100 points

# initialize a 2D array

D = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]

% these three examples accomplish the

% same thing

 

D = [1 2 3; 4 5 6; 7 8 9];

D = [1:3; 4:6; 7:9];

D = [1 2 3

4 5 6

7 8 9];

# print element of 2D array
# array indexes start at 0print(D[1][1]) # row 2, column 2# prints 5 using D from above table cell
% array indexes start at 1
D(2,2) % row 2, column 2
% prints 5 using D from above table cell
# print selected sub array of 2D array
# e.g., print rows 1 to 2 of column 1for i in range(0,2):
print(D[i][0])
 

D(1:2,1) % rows 1 to 2 of column 1

# print all rows of column 1 of 2D

# array

import numpy as np
D = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
Dsub = D[0:,0:1]
print(Dsub)

 

 

D(:,1) % all rows, column 1

# logical expression

a = 1
b = 2
if a == 1 or  b == 3:
print(‘a = 2 or b = 3’)

a = 1b = 2;

if a == 1 || b == 3

fprintf(‘a = 2 or b = 3 \n’);

end

# if structure

if a == 1 and b != 3:
print(‘a=1 and b not 3’);
print(‘OK?’)

 

if a == 1 && b ~= 3

fprintf(‘a=1 and b not 3 \n’);

fprintf(‘OK? \n’);

end

# if, else structure

if a != 1:
print(‘a is not 1’)
elif b != 3:
print(‘b is not 3’)
else:
print(‘huh?’)

 

a ~= 1

fprintf(‘a is not 1 \n’)
elseif b ~= 3

fprintf(‘b is not 3 \n’)

else

fprintf(‘huh? \n’)

end

# switch structure

# Python doesn’t have a switch structure

# any switch structure can be
# written as an if-else structure

# switch structures may be quicker to
# read and write for applications such as menus

 

switch menuChoice

case 1

% can do any actions in a case, e.g.,
% call a user-defined function

myMenuFunc01();
case 2

myMenuFunc02();

case 3

myMenuFunc03();

otherwise

fprintf(‘invalid selection, try again’)

end

# program which calls a user-defined function

# define function, here I chose name myfunc

def myfunc(x,y):
return x**y # ** is exponentiation operator

# call function

z = myfunc(2,3)
print(z)
# prints 8 for this input

% main program and function definition must

% be in separate files and function file

% must have same name as function name

 

z = myfunc(2,3)

% prints 8 for this input

 

—– LISTING OF FILE myfunc.m ——

function returnValue = myfunc(x,y)

returnValue = x^y; % ^ is exponentiation operator

% function is a keyword
% returnValue is arbitrary variable name

# matrix multiplication

import numpy as np

A = np.matrix( ((2,3), (3, 5)) )
B = np.matrix( ((1,2), (5, -1)) )

C = A * B
print(C)

 

 

 

A = [2,3; 3,5];

B = [1,2; 5,-1];
C = A * B

# plotting

import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0,2*np.pi,100)
y = np.sin(x)
plt.plot(x,y)
plt.ylabel(‘sin(x)’)
plt.xlabel(‘x’)
plt.show()

x = linspace(0,2*pi,100);
y = sin(x);
plot(x,y)
ylabel(‘sin(x)’)
xlabel(‘x’)

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