Machine Learning with MATLAB |
Build predictive models and discover useful patterns from
observed data.
Learn how to get started using machine learning tools to
detect patterns and build predictive models from your data sets.
Machine learning algorithms use computational methods to “learn” information directly from data without assuming a predetermined equation as a model. They can adaptively improve their performance as you increase the number of samples available for learning.
Machine learning algorithms are used in applications such as computational finance (credit scoring and algorithmic trading), computational biology (tumor detection, drug discovery, and DNA sequencing), energy production (price and load forecasting), natural language processing, speech and image recognition, and advertising and recommendation systems.
Machine learning is often used in big data applications, which have large datasets with many predictors (features) and are too complex for a simple parametric model. Examples of big data applications include forecasting electricity load with a neural network, or bond rating classification for credit risk using an ensemble of decision trees.
Build models to classify data into different categories.
Build models to predict continuous data.
Find natural groupings and patterns in data.
Algorithms: support vector machine (SVM), boosted and bagged decision trees, k-nearest neighbor, Naïve Bayes, discriminant analysis, neural networks, and more
Algorithms: linear model, nonlinear model, regularization, stepwise regression, boosted and bagged decision trees, neural networks, adaptive neuro-fuzzy learning, and more
Applications: credit scoring, tumor detection, image recognition
Applications: electricity load forecasting, algorithmic trading
Applications: pattern mining, medical imaging, object recognition
For more information on solving machine learning problems, see Statistics Toolbox™, Neural Network Toolbox™ and Fuzzy Logic Toolbox™.
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