Objective: Bring data into MATLAB and organize it for analysis, including normalizing data and removing observations with missing values. Process raw imported data by extracting, manipulating, aggregating, and counting portions of data.

• Create and modify tables and categorical arrays
• Process data with missing elements
• Create and modify categorical arrays
• Aggregate, bin, and count groups of data
• Process time-series data with timetables
• Use tall arrays to process big data

Objective: Use unsupervised learning techniques to group observations based on a set of explanatory variables and discover natural patterns in a data set.

• Unsupervised learning
• Clustering methods
• Cluster evaluation and interpretation

Objective: Use supervised learning techniques to perform predictive modeling for classification problems. Evaluate the accuracy of a predictive model.

• Supervised learning
• Training and validation
• Classification methods

Objective: Reduce the dimensionality of a data set. Improve and simplify machine learning models.

• Cross validation
• Feature transformation
• Feature selection
• Ensemble learning

Objective: Interactively explore and visualize signal processing features in data.

• Import, visualize, and browse signals to gain insights
• Perform signal measurements
• Compare multiple signals in the time and frequency domains
• Perform interactive spectral analysis
• Extract regions of interest for focused analysis
• Generate MATLAB scripts for automation

Objective: Learn techniques to clean signal sets with operations such as resampling, removing outliers, and filling gaps. Interactively generate and rank features.

• Use resampling to handle randomly sampled signals
• Fill gaps in uniformly sampled signals
• Perform resampling to ensure common time base across signals
• Use the Signal Analyzer app to design and apply filter
• Perform machinery diagnosis using envelope spectrum
• Locate outliers and replace with acceptable samples
• Detect changepoints and perform automatic signal segmentation

Objective: Explore data to identify features and train decision models to predict remaining useful life.

• Organize and label data using data ensembles
• Design condition indicator
• Estimate remaining useful life using estimator models and dynamic models

Objective: Use App Designer to create and share an app by laying out components and giving them interactive behavior.

• Discover the App Designer interface
• Add callbacks to components
• Program callback functionality
• Share apps with others

Objective: Create apps with graphical elements that can be modified by user interaction, as well as apps with continuously adjustable components.

• Use plotting functions within apps
• Access graphical objects within apps
• Program components to update the app continuously
• Share callbacks between multiple components
• Add helper and startup functionality
• Deploy web apps