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Getting Started with MATLAB Machine Learning
Rating: 4.0 out of 5(45 ratings)
188 students

Getting Started with MATLAB Machine Learning

Easily extract patterns and knowledge from your data using MATLAB
Created byPackt Publishing
Last updated 12/2017
English

What you'll learn

  • Learn the introductory concepts of machine learning
  • Explore the different types of regression technique such as simple and multiple linear regression, ordinary least squares estimation, correlations, and how to apply them to your data
  • Discover the basics of classification methods and how to implement the Naive Bayes algorithm and Decision Trees in the MATLAB environment
  • Perform data fitting, pattern recognition, and clustering analysis with the help of the MATLAB neural network toolbox

Course content

3 sections • 10 lectures • 1h 48m total length

  • The Course Overview3:23

    This video will give you an overview about the course.

  • Familiarizing Yourself with the MATLAB Desktop10:41

    MATLAB will show us a desk of contents with all the necessary items for its proper and smooth operation.

  • Importing Data into MATLAB15:37

    In this video, we will start from data collection and import in MATLAB for whatever analysis we are going to do. As well, we will finish our activities by exporting the results.

  • Exporting Data from MATLAB8:32

    Many of the functions we have used to import data into MATLAB have a corresponding function that allows us to export data. In this video, we will see those functions.

  • Data Organization14:59

    So far, for data organization, we have mostly used standard arrays that represent useful data structures for storing a large number of objects, but all of the same type, such as a matrix of numbers or characters. However, such arrays cannot be used if you want to memorize both numbers and strings in the same object. This is a problem that can be solved by so-called cell arrays, structure arrays, and more generally all those structures that the MATLAB programming environment provides us.

  • Importing and Organizing Data in MATLAB

Requirements

  • A mathematical and statistical background will really make this video easier to follow.

Description

MATLAB is the language of choice for many researchers and mathematics experts when it comes to machine learning. This video will help beginners build a foundation in machine learning using MATLAB. You'll start by getting your system ready with the MATLAB environment for machine learning and you'll see how to easily interact with the MATLAB workspace. You'll then move on to data cleansing, mining, and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll learn about the different types of regression technique and how to apply them to your data using the MATLAB functions. You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction to improve performance. By the end of the video, you'll have learned to put it all together via real-world use cases covering the major machine learning algorithms and will be comfortable in performing machine learning with MATLAB.

About the Author

Giuseppe Ciaburro holds a Master's degree in chemical engineering from Università degli Studi di Napoli Federico II, and a Master's degree in acoustic and noise control from Seconda Università degli Studi di Napoli. He works at the Built Environment Control Laboratory - Università degli Studi della Campania "Luigi Vanvitelli."

He has over 15 years' work experience in programming, first in the field of combustion and then in acoustics and noise control. His core programming knowledge is in Python and R, and he has extensive experience of working with MATLAB. An expert in acoustics and noise control, Giuseppe has wide experience in teaching professional computer courses (about 15 years), dealing with e-learning as an author. He has several publications to his credit: monographs, scientific journals, and thematic conferences. He is currently researching Machine Learning applications in acoustics and noise control.

Who this course is for:

  • This video is for data analysts, data scientists, students, or anyone keen to get started with machine learning and build efficient data processing and predictive applications.

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