Using MATLAB®, engineers and other domain experts have deployed thousands of applications for predictive maintenance, sensor analytics, finance, and communication electronics. 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
- All popular classification, regression, and clustering algorithms for supervised and unsupervised learning
- Faster execution than open source on most statistical and machine learning computations
Start Using MATLAB for Machine Learning
Watch a Demonstration
Read and Explore the Basics
Walk Through an Introduction
Test-drive the Classification Learner app.
Use the Classification Learner app to try different classifiers on your dataset. Fit common models like decision trees, support vector machines, ensembles, and more. Compare models using ROC curves and confusion matrices.
Try it on the Fisher Iris dataset: Can you find a model with high accuracy?