Machine learning in MATLAB is a dynamic field where you can develop predictive models using algorithms and techniques. Here’s an overview:

1. Toolboxes and Functions: MATLAB provides various toolboxes like the Statistics and Machine Learning Toolbox, Deep Learning Toolbox, and others that contain functions and apps to create, train, and validate predictive models.
2. Data Preprocessing: You can use MATLAB to preprocess and clean your data. Functions such as fillmissing, normalize, and standardizeMissing are available.
3. Creating Models: MATLAB offers functions to create different machine learning models, including supervised and unsupervised learning models. For example, you can create linear regression models, support vector machines (SVM), decision trees, and neural networks.
4. Training Models: You can train the models using methods like fitcsvm for SVM, fitctree for decision trees, and so on.
5. Evaluating Models: MATLAB provides tools to evaluate the models through different metrics and visualization tools such as confusion matrices, ROC curves, etc.
6. Deployment: Once the model is trained and validated, you can deploy it using MATLAB Compiler and other related tools to integrate it with other software or create standalone applications.
7. Deep Learning: MATLAB’s Deep Learning Toolbox provides comprehensive tools to design, analyze, and train deep learning networks.
8. Apps: There are various interactive apps like the Classification Learner and Regression Learner that guide you through the steps of creating, validating, and exporting models.