How do you deal with data that’s messy, incomplete, or in varied formats? How do you choose the right model for the data?
The solution to these questions is MATLAB.
MATLAB is the language of choice for many researchers and mathematics experts when it comes to machine learning. Engineers and data scientists work with large amounts of data in a variety of formats such as sensor, image, video, telemetry, databases, and much more. They use machine learning to find patterns in data and to build models that predict future outcomes based on historical data. With MATLAB, you have immediate access to prebuilt functions, extensive toolboxes, and specialized apps for classification, regression, and clustering. MATLAB is designed to give developers fluency in MATLAB programming language. Problem-based MATLAB examples have been given in simple and easy way to make your learning fast and effective. If you're interested to learn and implement powerful machine learning techniques, using MATLAB, then go for this Learning Path.
Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.
The highlights of this Learning Path are:
- Explore the different types of regression techniques such as simple and multiple linear regression, ordinary least squares estimation, correlations, and how to apply them to your data
- Perform data fitting, pattern recognition, and clustering analysis with the help of the MATLAB neural network toolbox.
- Use feature selection and extraction for dimensionality reduction, leading to improved performance.
Let’s take a quick look at your learning journey. This Learning Path will help you build a foundation in machine learning using MATLAB. You'll start by getting your system ready with the MATLAB environment for machine learning and 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. You’ll also learn to display data values on a plot. Next, you'll learn about the different types of regression techniques 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. You'll also explore feature selection and extraction techniques for dimensionality reduction to improve performance. Finally, you’ll learn to put it all together through real-world use cases covering major machine learning algorithms and will now be an expert in performing machine learning with MATLAB.
By the end of this Learning Path, you'll have acquired a complete knowledge on powerful machine learning techniques of MATLAB
Meet Your Expert:
We have combined the best works of the following esteemed author to ensure that your learning journey is smooth:
- Giuseppe Ciaburro holds a PhD in environmental technical physics and two master's degrees. His research was focused on machine learning applications in the study of the urban sound environments. He works at Built Environment Control Laboratory - UniversitàdegliStudidella Campania Luigi Vanvitelli (Italy). He has more than 15 years of work experience in programming (Python, R, and MATLAB), first in the field of combustion and then in acoustics and noise control. He has several publications to his credit.