Machine learning teaches machines to do what comes naturally to humans: learn from experience. Use MATLAB to engineer features from your data and fit machine learning models.
Why MATLAB for Machine Learning?
Classify Data Using the Classification Learner App
Interactively explore your data, select features, and train, compare, and assess models by using the Classification Learner and Regression Learner apps.
Integrate with Simulink Systems
Integrate your trained models with Simulink as native or MATLAB Function blocks, for embedded deployment or simulation of complete systems.
Deploy Trained Models to Hardware
Deploy your trained models to hardware platforms (from desktop systems to embedded hardware) by generating readable and portable C/C++ code.
Machine Learning Applications
With just a few lines of MATLAB code or with low-code apps, you can incorporate machine learning into your applications whether you are building models, engineering features, or generating code and deploying to embedded systems.
Signal Processing
Acquire and analyze signals and time-series data
Predictive Maintenance
Develop machine learning models to detect and predict faults
Computational Biology
Analyze and model data to classify and predict biological behavior
Computational Finance
Develop machine learning models for finance applications
Wireless
Apply AI techniques to wireless communications applications
Radar
Apply artificial intelligence techniques to radar applications
AI Applications with MATLAB and Simulink
Machine Learning with MATLAB Tutorials and Examples
Whether you are new to machine learning or looking for an end-to-end workflow, explore these MATLAB resources to help with your next project.
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Statistics and Machine Learning Toolbox
Analyze and model data using statistics and machine learning
MATLAB for Deep Learning
Design and implement deep neural networks with algorithms and pretrained models
