What are some scalable machine learning algorithms?

What are some scalable machine learning algorithms I can use for large datasets?

Some popular scalable machine learning algorithms include Random Forests, Gradient Boosting Machines, and Support Vector Machines.

How do these algorithms handle large amounts of data?

They use techniques like parallel processing and mini-batch training to efficiently manage and process large datasets.

Can you explain how Gradient Boosting Machines work in a scalable way?

Gradient Boosting Machines build models in stages, optimizing the loss function while allowing for parallelization of tree construction.

Are there any specific libraries or frameworks that support these algorithms?

Yes, libraries like Apache Spark MLlib, Scikit-learn, and XGBoost are great for implementing scalable machine learning algorithms.