Learning Machine Learning - Spark MLlib and Spark ML

"Each of us, actually every animal, is a data scientist. We collect data from our sensors, and then we process the data to get abstract rules to perceive our environment and control our actions in that environment to minimize pain and/or maximize pleasure. We have memory to store those rules in our brains, and then we recall and use them when needed. Learning is lifelong; we forget rules when they no longer apply or revise them when the environment changes."

- Ethem Alpaydin, Machine Learning: The New AI

The purpose of this chapter is to provide a conceptual introduction to statistical machine learning (ML) techniques for those who might not normally be exposed to such approaches during their typical required statistical training. This chapter also aims to take a newcomer from having minimal knowledge of machine learning all the way to being a knowledgeable practitioner in a few steps. We will focus on Spark's machine learning APIs, called Spark MLlib and ML, in theoretical and practical ways. Furthermore, we will provide some examples covering feature extraction and transformation, dimensionality reduction, regression, and classification analysis. In a nutshell, we will cover the following topics in this chapter:

Introduction to machine learning
Spark machine learning APIs
Feature extractor and transformation
Dimensionality reduction using PCA for regression
Binary and multiclass classification

Table of Contents for Learning Machine Learning - Spark MLlib and Spark ML

Create new playlist

Sign In

Sign Up

Table of Contents for
Learning Machine Learning - Spark MLlib and Spark ML