Using MATLAB®, engineers and other domain experts have deployed thousands of machine learning applications. MATLAB makes the hard parts of machine learning easy with:
Point-and-click apps for training and comparing models
Advanced signal processing and feature extraction techniques
Automatic hyperparameter tuning and feature selection to optimize model performance
The ability to use the same code to scale processing to big data and clusters
Automated generation of C/C++ code for embedded and high-performance applications
Popular classification, regression, and clustering algorithms for supervised and unsupervised learning
Faster execution than open source on most statistical and machine learning computations
See How Others Use MATLAB for Machine Learning
Main Features of Machine Learning
Interactive Apps and Algotithms
Choose from a wide variety of the most popular classification, clustering, and regression algorithms. Use classification and regression apps to interactively train, compare, tune, and export models for further analysis, integration, and deployment. If writing code is more your style, you can further optimize models with feature selection and parameter tuning
Automated Machine Learning (AML)
Automatically generate features from training data and optimize models using hyperparameter tuning techniques such as Bayesian optimization. Use specialized feature extraction techniques such as wavelet scattering for signal or image data, and feature selection techniques such as neighborhood component analysis (NCA) or sequential feature selection.
Deploy statistics and machine learning models to embedded systems by generating readable C or C++ code for your entire machine learning algorithm, including preprocessing and post-processing. Update parameters of deployed models without regenerating the C/C++ prediction code. Accelerate verification and validation of your high-fidelity simulations using machine learning models through MATLAB function blocks and system blocks in Simulink®.
Scaling and Performance
Use tall arrays to train machine learning models on data sets too large to fit in machine memory, with minimal changes to your code. You can also speed up statistical computations and model training with parallel computing on your desktop, on clusters, or on the cloud.