What’s New in MATLAB for Data Science
MATLAB makes data science easy and accessible for everyone,
even if you’re not an expert. Check out the latest features for designing
and building machine learning models, working with big data, and deployment.
Developing Machine Learning Models
- Regression Learner App: Train regression models using supervised machine learning.
- Classification Learner App: Train classification models using supervised machine learning.
- Text Analytics: Analyze and model text data.
- Bayesian Optimization: Tune machine learning algorithms by searching for optimal hyperparameters.
- Feature Selection: Use neighborhood component analysis (NCA) to choose features for machine learning models.
Working with Big Data
tallArrays for Big Data: Manipulate and analyze data that is too big to fit in memory.
- Big Data Algorithms: Perform support vector machine (SVM) and Naive Bayes classification, create bags of decision trees, and fit lasso regression on out-of-memory data.
- Big Data Plots: Visualize out-of-memory data using
- Deploying Big Data Applications: Run applications on your desktop or Spark using tall arrays or the MATLAB API for Spark.
Managing and Preprocessing Data
timetableData Container: Manage time-stamped tabular data with time-based indexing and synchronization.
stringArray: Manipulate, compare, and store text data efficiently.
- Preprocessing Data: Find, fill, and remove missing data. Detect and replace outliers. Smooth noisy data
- Access Data in the Cloud: Read data from Amazon S3 and Azure Blob Storage.
- Code Generation: Generate C code for prediction by using discriminant analysis, k-NN, SVM regression, regression tree ensemble, and Gaussian process regression models.
- RESTful API and JSON: Develop clients for MATLAB Production Server in any programming language that supports HTTP.
- Server Management Dashboard: Configure and manage multiple server instances using a web-based interface.