Learn MATLAB Programming for Engineers | MATLAB Training

Learn MATLAB Programming for Engineers | MATLAB Training

Learn to use MATLAB for problem solving, run scripts, write code and do data analysis and visualization, solve equations, do math operations and manipulate matrices, and formulate your own logic and convert complex problems into MATLAB code and solve them using programming skills.

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 advance 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 thresholds, you will definitely 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: Advance 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
Your Benefits and Advantages:

If you do not find the course useful, you are covered with 30 day money back guarantee, full refund, no questions asked!
You will be sure of receiving quality contents since the instructors has already four courses in the MATLAB niche which are ranked in the top 10 courses in the MATLAB niche
You have lifetime access to the course
You have instant and free access to any updates i add to the course
You have access to all Questions and discussions initiated by other students
You will receive my support regarding any issues related to the course
Check out the curriculum and Freely available lectures for a quick insight
It's time to take Action!

Click the "Add to Cart" button at the top right now!

Time is limited and Every second of every day is valuable.

We are excited to see you in the course!

Best Regrads,

Dr. Nouman Azam

More Benefits and Advantages:

You receive knowledge from an experienced instructor (Dr. Nouman Azam) who is the creator of five courses on Simpliv in the MATLAB niche.
The titles of these courses are
MATLAB from A to Z: From Programming to App Desiging
Data Analysis with MATLAB for EXCEL Users.
MATLAB App Desigining: The Ultimate Guide for MATLAB Apps
Create Apps in MATLAB with App Designer (Codes Included)
Advance MATLAB Data Types and Data Structures
You find majority of these courses on the first page in the MATLAB niche
Student Testimonials for Dr. Nouman Azam!

This is the second Simpliv class on Matlab I've taken. Already, a couple important concepts have been discussed that weren't discussed in the previous course. I'm glad the instructor is comparing Matlab to Excel, which is the tool I've been using and have been frustrated with. This course is a little more advanced than the previous course I took. As an engineer, I'm delighted it covers complex numbers, derivatives, and integrals. I'm also glad it covers the GUI creation. None of those topics were covered in the more basic introduction I first took.

Jeff Philips

Great information and not talking too much, basically he is very concise and so you cover a good amount of content quickly and without getting fed up!

Oamar Kanji

The course is amazing and covers so much. I love the updates. Course delivers more then advertised. Thank you!

Josh Nicassio

Student Testimonials! who are also instructors in the MATLAB category

"Concepts are explained very well, Keep it up Sir...!!!"

Engr Muhammad Absar Ul Haq instructor of course "Matlab keystone skills for Mathematics (Matrices & Arrays)"

Who is the target audience?

Researchers, Entrepreneurs, Instructors, College Students, Engineers, Programmers, Simulators who wants to quickly create front ends for their users to run their code and projects
Basic knowledge
The students must install MATLAB on their computers
The course is self explainatory and do not need any prior knowledge of MATLAB
To continue:

Tutorial How to write MatLab functions in Python

Tutorial How to write MatLab functions in Python

A tutorial on writing MatLab-like functions using the Python language and the NumPy library.


Recently in my work, I was re-writing algorithms developed in MatLab to Python, some functions are not so simple to adapt, especially the array functions that are called Cell Arrays.

MatLab has an API where you can call MatLab functions via Python. The idea, however, was not to use MatLab, but the same algorithm works the same way using only Python and NumPy, and the GNU Octave also has an API similar to that of MatLab.

To maintain compatibility, I have created functions with the same name that are used in MatLab that is encapsulated in a class called Precision.

1. Testing

Make the repository clone and follow the instructions in the README file:

Below I will show some examples, these are contained in the unit tests.

1.1 Start Stopwatch Time

Measuring the time spent in processing.

from precision import Precision

p = Precision()
for i in range(0, 1000): print(i)

The output will look something like this:

: > Elapsed time is 0:0:2 secounds.

1.2 Percentiles of a Data Set

This is used to get a percentile. In the example below, we are creating a range of ordinal dates by cutting 5% from the left and 5% from the right.

from datetime import datetime
from precision import Precision

p = Precision()
d = [i for i in p.dtrange(datetime(2018, 6, 12), 
                          datetime(2059, 12, 12), 
                          {'days':1, 'hours':2})]
x = [p.datenum(i.date()) for i in d]

x1 = p.prctile(x, 5)
x2 = p.prctile(x, 95)
r = (x2 - x1)

The output will look something like this:

5% lower: 737980.1
5% higher: 751621.9
delta: 13641.800000000047

1.3 Cell Array (cell2mat)

This converts a cell array to an ordinary array of the underlying data type.

from precision import Precision

p = Precision()
p.cell2mat([[1, 2], [3, 4]])
p.cell2mat('1 2; 3 4')

The output will look something like this:

matrix([[1, 2],
        [3, 4]])

1.4 Cell Array (num2cell)

Convert array to cell array with consistently sized cells.

import numpy
from precision import Precision

p = Precision()
x = numpy.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], numpy.int64)

The output will look something like this:

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

1.5 Concatenate Strings (strcat)

This concatenates strings horizontally using strcat.

import pandas
from precision import Precision

p = Precision()
df = pandas.DataFrame(data={'A': [1, 2], 'B': [3, 4]}, dtype=numpy.int8)
p.strcat(df, 'B')

The output will look something like this:

['3', '4']

1.6 Histogram (histc)

This counts the number of values in x that are within each specified bin range. The input, binranges, determines the endpoints for each bin. The output, bincounts, contains the number of elements from x in each bin.

import numpy 
from precision import Precision

p = Precision()
v = numpy.array([[1.5, 2.0, 3], [4, 5.9, 6]], numpy.int64)
p.histc(v, numpy.amax(v) + 1)

The output will look something like this:

(array([1, 1, 1, 0, 1, 1, 1]), array([1., 1.71428571, 2.42857143, 
       3.14285714, 3.85714286, 4.57142857, 5.28571429, 6.]))

1.7 Unique

Looking for unique values in an array and returning the indexes, inverse, and counts.

import numpy 
from precision import Precision

p = Precision()
x = [0, 1, 1, 2, 3, 4, 4, 5, 5, 6, 7, 7, 7]

The output will look something like this:

array([[array([0, 1, 2, 3, 4, 5, 6, 7]),
        array([[ 0,  1,  3,  4,  5,  7,  9, 10]]),
        array([0, 1, 1, 2, 3, 4, 4, 5, 5, 6, 7, 7, 7]),
        array([1, 2, 1, 1, 2, 2, 1, 3])]], dtype=object)

1.8 Overlaps

Looking for the overlays between two arrays returning the index.

import numpy 
from precision import Precision

p = Precision()
x, y = p.overlap2d(numpy.array(['A','B','B','C']), 

The output will look something like this:

(array([0, 1, 2, 3]), array([1, 2, 0, 3]))


There are functions that are not exactly MatLab but will serve as support, I hope it can help someone. There is an interesting article in NumPy for users who are migrating from MatLab to Python.

Further Reading

MATLAB vs Python: Why and How to Make the Switch

Creating a Plot Charts in Python with Matplotlib

Python Tutorial - Python GUI Programming - Python GUI Examples (Tkinter Tutorial)

Essential Python 3 code for lists

*Originally published by Ederson Corbari   at *dzone.com


Thanks for reading :heart: If you liked this post, share it with all of your programming buddies! Follow me on Facebook | Twitter

Python Programming Tutorials For Beginners

Python Programming Tutorials For Beginners

Python Programming Tutorials For Beginners

Hello and welcome to brand new series of wiredwiki. In this series i will teach you guys all you need to know about python. This series is designed for beginners but that doesn't means that i will not talk about the advanced stuff as well.

As you may all know by now that my approach of teaching is very simple and straightforward.In this series i will be talking about the all the things you need to know to jump start you python programming skills. This series is designed for noobs who are totally new to programming, so if you don't know any thing about

programming than this is the way to go guys Here is the links to all the videos that i will upload in this whole series.

In this video i will talk about all the basic introduction you need to know about python, which python version to choose, how to install python, how to get around with the interface, how to code your first program. Than we will talk about operators, expressions, numbers, strings, boo leans, lists, dictionaries, tuples and than inputs in python. With

Lots of exercises and more fun stuff, let's get started.

Download free Exercise files.

Dropbox: https://bit.ly/2AW7FYF

Who is the target audience?

First time Python programmers
Students and Teachers
IT pros who want to learn to code
Aspiring data scientists who want to add Python to their tool arsenal
Basic knowledge
Students should be comfortable working in the PC or Mac operating system
What will you learn
know basic programming concept and skill
build 6 text-based application using python
be able to learn other programming languages
be able to build sophisticated system using python in the future

To know more:

MATLAB Numpy element wise multiplication issue

Please see the MATLAB code and equivalent Numpy code below. Question: How can I get the D variable same in Numpy as MATLAB's?

Please see the MATLAB code and equivalent Numpy code below. Question: How can I get the D variable same in Numpy as MATLAB's?


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

C = [100 1; 10 0.1; 1, 0.01]

C = reshape(C, 1,3,2)

D = bsxfun(@times, A, C)

D(:,:,1) =

   100    20     3
   400    50     6
   700    80     9

D(:,:,2) =

1.0000    0.2000    0.0300
4.0000    0.5000    0.0600
7.0000    0.8000    0.0900

Numpy Code

A = np.array([[1,2,3],[4,5,6],[7,8,9]])

C = np.array([[[100, 1], [10, 0.1], [1, 0.01]]]) # C.shape is (1, 3, 2)

D = A * C.T


array([[[100.  , 200.  , 300.  ],
        [ 40.  ,  50.  ,  60.  ],
        [  7.  ,   8.  ,   9.  ]],

       [[  1.  ,   2.  ,   3.  ],
        [  0.4 ,   0.5 ,   0.6 ],
        [  0.07,   0.08,   0.09]]])