Increasing Complexity with Linear and Nonlinear Tricks
IN THIS CHAPTER
Expanding your feature using polynomials
Regularizing your model
Learning from big data
Using support vector machines and neural network
Previous chapters introduce you to some of the simplest, yet effective, machine learning algorithms, such as linear and logistic regression, Naïve Bayes, and K-Nearest Neighbors (KNN). At this point, you can successfully complete a regression or classification project in data science. This chapter explores even more complex and powerful machine learning techniques including the following: reasoning on how to enhance your data; improving your estimates by regularization; and learning from big data by breaking it into manageable chunks.
This chapter also introduces you to the support vector machine (SVM), a powerful family of algorithms for classification and regression. The chapter touches on neural networks as well. Both SVMs and neural networks can perform the most difficult data problems in data science. Initially introduced a few years ago as a replacement for ...