Matlab Tutorial Free

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Matlab Tutorial Free Average ratng: 3,9/5 1521 reviews

A more recent version is available as course 18.S997 Introduction To MATLAB Programming, including video lectures. The course, intended for students with no programming experience, provides the foundations of programming in MATLAB®. Variables, arrays, conditional statements, loops, functions, and plots are explained. By my knowledge there are three routes you can take: 1. The most obvious: download it illegally. This is the same as stealing from the mathworks company and has the obvious moral and legal issues. I will give you no advice on how to do this.

Basic Course Description

MATLAB (matrix laboratory) is one of the fundamental and leading programming language and is a must learn skill for anyone who want to develop a career in engineering, science or related fields. Excellent MATLAB programming skills is therefore a crucial factor in making or breaking your career.

This course is designed from a perspective of a student who has no prior knowledge of MATLAB. The course starts from the very basic concepts and then built on top of those basic concepts and move towards more advanced topics such as visualization, exporting and importing of data, advance data types and data structures and advance programming constructs.

To get the real feel of MATLAB in solving and analyzing real life problems, the course includes machine learning topics in data science and data preprocessing. To convert the source codes into meaningful pieces of softwares, the course also covers topics in building GUI's using GUIDE and App Designer utilities of matlab. Finally, we also cover topics in text processing such as building Regular Expressions.

The course is fun and exciting, but at the same time we dive deep into MATLAB to uncover its power of formulating and analyzing real life problems. Norton 60 day free trial. The course is structured into four different Parts. Below is the detailed outline of this course.

Part 1: MATLAB from Beginer to Advance

Segment 1.1: Handling variables and Creating Scripts

Segment 1.2: Doing Basic Maths in MATLAB

Segment 1.3: Operations on Matrices

Segment 1.4: Advance Math Functions with Symbolic Data Type

Segment 1.5: Interacting with MATLAB and Graphics

Segment 1.6: Importing Data into MATLAB

Segment 1.7: File Handling and Text Processing

Segment 1.8: MATLAB Programming

Segment 1.9: Sharing Your MATLAB Results

Part 2: Advance MATLAB Data Types

Segment 2.1: Cell Data Type

Segment 2.2: Tables and Time Tables

Segment 2.3: Working with Structures and Map Container Data Type

Segment 2.4: Converting between Different Data Types


Part 3: Machine Learning for Data Science Using MATLAB

Segment 3.1 Data Preprocessing

Segment 3.2. Classification

Segment 3.2.1 K-Nearest Neighbor

Segment 3.2.2 Naive Bayes

Segment 3.2.3 Decision Trees

Segment 3.2.4 Support Vector Machine

Segment 3.2.5 Discriminant Analysis

Segment 3.2.6 Ensembles

Segment 3.2.7 Performance Evaluation

Segment 3.3 Clustering

Segment 3.3.1 K-Means

Segment 3.3.2 Hierarchical Clu stering

Segment 3.4 Dimensionality Reduction

Segment 3.5 Project


Part 4: Data Preprocessing for Machine Learning using MATLAB

Segment 4.1 Handing Missing Values

Segment 4.2 Dealing with Categorical Variables

Segment 4.3 Outlier Detection

Segment 4.4 Feature Scaling and Data Discretization

Segment 4.5 Selecting the Right Method for your Data


Part 5: Build Regular Expression using MATLAB for Text Processing

Segment 5.1 : Introduction and getting started with regexes

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Segment 5.2: Character Classes

Segment 5.3: Anchors and Word Boundaries

Segment 5.4: Repetitiongs using Quantifiers

Segment 5.5: Group Constructs

Segment 5.6: Assertions, Conditions and Backreferencing

Segment 5.7: Practical Examples


Part 6: Matlab App Designing Using Guide

Segment 6.1: Basics of the GUIDE

Segment 6.2: Linking the Code with GUI

Segment 6.3: Advance Techniques for GUIDE

Segment 6.4: Sample Projects with GUIDE

Segment 6.5: More Useful Tricks and Examples with GUIDE


Part 7: Create MATLAB Apps with App DesignerXploder download free.

Segment 7.1: Basics of App Designer

Segment 7.2: Tips and Tricks for Effective use of App Designer

Segment 7.3: Coding GUI's

Segment 7.4: Advance Techniques

Segment 7.5: Sample Projects with App Designer


  • Anyone looking to build a strong career in science or engineering through Excellent MATLAB coding skills
  • Anyone wanting to advance their skills of real world problem solving with MATLAB based scientific computing

Welcome to the Matlab on Windows tutorial. This tutorial will give you the ability to log or stream data from a MetaSensor to Matlab.

9.1. Prerequsites¶

You are required to have access to Matlab with a current license for this tutorial. Typically, your school or university will have Matlab available to you for free. You can purchase and download Matlab here: https://www.mathworks.com/products/matlab.html

You should also be familiar with the MetaBase App for Windows. If you have not downloaded our free App yet, please refer to this section of the tutorial: https://mbientlab.com/tutorials/MetaBaseApp.html

Please note that our tutorial has only been tested on Windows 10 and Matlab R2018a.

Warning

Matlab does not support Bluetooth Low Energy and this tutorial provides a work around to send MetaSensor data to Matlab.

Follow the steps at: https://www.mathworks.com/help/install/introduction-to-installation-licensing-and-activation.html.

9.2. Log Temperature Data¶

Using the Windows MetaBase App, the MetaSensors is configured to log sensor data.

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Please refer to the MetaBase section of the tutorial if you need additional help.

In this example, temperature on the MetaSensor was logged for a few minutes.

As per our MetaBase on Windows tutorial, the resulting data is available in your Downloads Folder as a .csv file:

Because the Matlab csv reader function csvread does not handle text very well, I removed the first two columns from the csv file with the epoch and timestamp data types using Excel. Only the time elapsed column will be used.

The following commands are used to read the csv file into Matlab:

As you can see, we read the csv file into the Data variable and we skip the first row because it is only text.The data is separated into the two columns: col1 is the time in seconds and col2 is the temperature data in degrees.

Finally the data is plotted:

9.3. Stream Gyroscope Data¶

Our streaming examples use Python to send data through the Bluetooth link to the Windows PC and then pipe data to Matlab using a socket.

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You are expected to have at least a basic understanding of Python as a language. You can go to the Python for Windows portion of the tutorial to learn more.

Download the examples from our Github: https://github.com/mbientlab/MetaWear-Tutorial-Matlab

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You can clone the repository or simply download it. The Tutorial repository includes two python scripts (.py files) and two Matlab scripts (.m files).

We will first use the echo_gyro_server.py python script to stream MetaSensors gyroscope data live from the Bluetooth dongle on a Windows PC via a TCP socket into Matlab.

First Python needs to setup the socket and get the MetaSensor ready. On the command line, type:

This Python script will open up a TCP socket on port 50007 of your local machine (Windows PC) and the script will wait until there is a response on the socket from Matlab.

Open up the real_time_gyro_data_stream_plotting.m file in Matlab.

Hit the RUN button or type this into the command line of Matlab:

The python script can now continue because the socket is open. The script configures the MetaSensors to stream accelerometer x,y,z data at 50Hz with the range set at 125dps:

The gyroscope x,y,z data is available in the callback in Matlab and stored in FIFOs (asynchronous buffers).

When the Matlab script ends, the x,y,z gyroscope data is graphed using the plot command:

9.4. Stream Accelerometer Data¶

Now we will use the echo_acc_server.py python script to stream MetaSensors accelerometer data live from the Bluetooth dongle on a Windows PC via a TCP socket into Matlab.

First Python needs to setup the socket and get the MetaSensor ready. On the command line, type:

This Python script will open up a TCP socket on port 50007 of your local machine (Windows PC) and the script will wait until there is a response on the socket from Matlab.

Open up the real_time_acc_data_stream_plotting.m file in Matlab.

Hit the RUN button or type this into the command line of Matlab:

The python script can now continue because the socket is open. The script configures the MetaSensors to stream accelerometer x,y,z data at 100Hz with the range set at 2Gs.

The accelerometer x,y,z data is ploted live (in real-time) in the callback in Matlab:

9.5. Stream Sensor Fusion Data¶

We will now use the echo_quat_server.py python script to stream MetaSensors Euler angles (roll, pitch, yaw) live from the Bluetooth dongle on a Windows PC via a TCP socket into Matlab.

First Python needs to setup the socket and get the MetaSensor ready. On the command line, type:

This Python script will open up a TCP socket on port 50007 of your local machine (Windows PC) and the script will wait until there is a response on the socket from Matlab.

Open up the real_time_quat_data_stream_plotting.m file in Matlab.

Hit the RUN button or type this into the command line of Matlab:

The Python script can now continue because the socket is open. The script configures the MetaSensors to stream accelerometer roll, pitch, yaw data (in degrees) at 100Hz:

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The sensor fusion data is available in the callback and ploted live in 3D.

As you move your MetaSensor around, you should see the sphere move in 3D on the plot as well.

We encourage you to play with this example and change the axes as needed.

Warning

Matlab can be slow and you may see delays when plotting/graphing data live.

If you look at the scripts, you will notice that you can uncomment a few specific lines and stream quaternion data instead of Euler angles. Feel free to give it a try.

9.6. Next steps¶

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Go and build your own Matlab and Python script!

We welcome pull requests on our Matlab Tutorial Github Repository: https://github.com/mbientlab/MetaWear-Tutorial-Matlab