Spark provides a machine learning library known as MLlib. Spark MLlib provides various machine learning algorithms such as classification, regression, clustering, and collaborative filtering. It also provides tools such as featurization, pipelines, persistence, and utilities for handling linear algebra operations, statistics and data handling. This course will start you off on your journey and walk you through some of the machine learning libraries and how to use them.
Overview
Syllabus
- Module 1 - Spark MLlib Datatypes
- Understand the difference between Dense and Sparse Data Types, and how they apply to LabeledPoints and matrices.
- Understand how to create and use the different matrices that are available in Spark MLlib.
- Module 2 - Review of Algorithms
- Have a general understanding of each of the algorithm that will be discussed in the course and how they work.
- Learn how to instantiate simple Linear Regression and Classification models, including Linear Regression, Support Vector Machines, and Logistic Regression.
- Module 3 - Spark MLlib Decision Trees and Random Forests
- Learn about the different input parameters used to create Decision Trees and Random Forests.
- Understand the effects of tuning specific parameters for Decision Trees and Random Forests.
- Module 4 - Spark MLlib Clustering
- Learn about the parameters involved in creating K-Means Clustering models and Gaussian Mixture Clustering models.
- Describe how outputs and uses of the functions available to each clustering model.
