This is the code repository for MATLAB for Machine Learning, second edition, published by Packt.
Unlock the power of deep learning for swift and enhanced results
Discover why the MATLAB programming environment is highly favored by researchers and math experts for machine learning with this guide which is designed to enhance your proficiency in both machine learning and deep learning using MATLAB, paving the way for advanced applications.
This book covers the following exciting features:
- Discover different ways to transform data into valuable insights
- Explore the different types of regression techniques
- Grasp the basics of classification through Naive Bayes and decision trees
- Use clustering to group data based on similarity measures
- Perform data fitting, pattern recognition, and cluster analysis
- Implement feature selection and extraction for dimensionality reduction
- Harness MATLAB tools for deep learning exploration
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
gscatter(meas(:,3), meas(:,4), species,'rgb','osd');
xlabel('Petal length');
ylabel('Petal width');
Following is what you need for this book: This book is for ML engineers, data scientists, DL engineers, and CV/NLP engineers who want to use MATLAB for machine learning and deep learning. A fundamental understanding of programming concepts is necessary to get started.
With the following software and hardware list you can run all code files present in the book (Chapter 1-11).
| Chapter | Software required | OS required |
|---|---|---|
| 1-11 | MATLAB | Any OS |
Giuseppe Ciaburro holds a PhD in environmental engineering physics and two master’s degrees in chemical engineering and in acoustics and noise control. He works at the Built Environment Control Laboratory at “Università degli studi della Campania Luigi Vanvitelli “ and has over 20 years of experience in programming, first in the field of combustion and then in acoustics and noise control. His core programming skills are in MATLAB, Python, and R. As an expert in acoustics and noise control, Giuseppe has extensive experience in teaching and researching. He is currently researching machine learning applications in acoustics and noise control. He has written for several publications, and for the last two years, he has been ranked by Stanford University as one of the top 2% of scientists in the world.