LEARNING PATH: MATLAB: Powerful Machine Learning with MATLAB
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- Learn the introductory concepts of machine learning
- Explore different ways to transform data using SAS XPORT, import, and export tools
- Discover the basics of classification methods and how to implement the Naive Bayes algorithm and decision trees in the MATLAB environment.
- Use clustering methods such as hierarchical clustering to group data using similarity measures
- Perform data fitting, pattern recognition, and clustering analysis with the help of the MATLAB neural network toolbox
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.
Before passing our data to machine learning algorithms, we need to give a first look at what we've imported into MATLAB to see if there are any issues. To get started, it's good practice to keep your original data. To do this, every change will be performed on a copy of the dataset.
We will explore decision trees methods. Then, we will learn the concepts like nodes, branches, and leaf nodes. We will see how to classify objects into a finite number of classes by repeatedly dividing the records into homogeneous subsets with respect to the target attribute.
With nearest neighbor classifiers, we will learn how to identify the class of a sample based on the distance of it from other classified objects. Discover how to fix the distance metric and how to choose the optimal value for K. So, as to understand how to improve model performance through cross-validation.
In this video, we will analyze the Classification Learner app, and how it leads us into step-by-step classification analysis. With the help of this app, to import and explore data, select features, specify validation schemes, train models, and evaluate results, will be extremely simple and fast.
We will look at a couple of methods for grouping objects: hierarchical clustering and partitioning clustering. In the first method, clusters are constructed by recursively partitioning the instances in either a top-down or bottom-up fashion. The second one decomposes a dataset into a set of disjoint clusters.
In this video, we will explore partitioning clustering through the k-means method. Then we will learn how to locate K - centroids, one for each cluster, by an iterative procedure. We will discover how well these clusters are separated and how to make a silhouette plot using cluster indices issued by K-means.
K-medoids is more robust to noise and outliers than K-means, because a mean is easily influenced by extreme values. We will learn to use the K-medoids function; it partitions the observations of a matrix into k clusters and returns a vector containing the cluster indices of each observation.
The models are composed of k (positive integer) multivariate normal density components. Each component has an n-dimensional mean, n-by-n covariance matrix, and mixing proportion. We will use the fitgmdist function to return a Gaussian mixture distribution model with k components (fixed by the user) fitted to the input dataset.
To make the application of neural networks as simple as possible, the toolbox gives us a series of GUIs. In this video, We will check out the neural network getting started GUI, the starting point for our neural network fitting, pattern recognition, clustering, and time series analysis.
Selection of features is necessary to create a functional model so as to achieve a reduction in cardinality, imposing a limit greater than the number of features that must be considered during its creation. Feature selection is based on finding a subset of the original variables, usually iteratively, thus detecting new combinations of variables and comparing prediction errors.
- Basic knowledge MATLAB is needed
- Basic mathematical and statistical background is assumed
- Basic programming knowledge of C, C++, Java, and Python is needed
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.
- This Learning Path is for data analysts, data scientists, students, or anyone keen to get started with machine learning added with MATLAB and build efficient data processing and predictive applications.