Chapter 7. Getting smart with MLlib
This chapter covers
- Machine-learning basics
- Performing linear algebra in Spark
- Scaling and normalizing features
- Training and applying a linear regression model
- Evaluating the model’s performance
- Using regularization
- Optimizing linear regression
Machine learning is a scientific discipline that studies the use and development of algorithms that make computers accomplish complicated tasks without explicitly programming them. That is, the algorithms eventually learn how they can solve a given task. These algorithms include methods and techniques from statistics, probability, and information theory.
Today, machine learning is ubiquitous. Examples include online stores that offer you similar items that ...