Main Content

AI, Data Science, and Statistics

Data preparation, design, simulation, and deployment for machine learning and deep neural networks

MATLAB® makes data science easy with tools to access and preprocess data, build machine learning and predictive models, and deploy models.

Using apps or with just a few lines of MATLAB code, you can apply statistical, machine, and deep learning techniques to your work for designing algorithms, preparing and labeling data, or generating code and deploying to embedded systems. Extend AI modeling and data fitting workflows with specialized tools for:

  • Data types such as images, video, signals, audio, and text

  • Applications such as computer vision, audio and signal processing, text analytics, wireless communications, and automated driving.

Workflow for AI from data preparation to modeling to system design and deployment


AI Basics

  • Machine Learning in MATLAB (Statistics and Machine Learning Toolbox)
    Discover machine learning capabilities in MATLAB for classification, regression, clustering, and deep learning, including apps for automated model training and code generation.
  • Deep Learning in MATLAB (Deep Learning Toolbox)
    Discover deep learning capabilities in MATLAB using convolutional neural networks for classification and regression, including pretrained networks and transfer learning, and training on GPUs, CPUs, clusters, and clouds.
  • What Is Reinforcement Learning? (Reinforcement Learning Toolbox)
    Reinforcement learning is a goal-directed computational approach where a computer learns to perform a task by interacting with an uncertain dynamic environment.

AI Modeling

Simulation and Deployment