Python for Data Science For Dummies, 3rd Edition

Chapter 19

Increasing Complexity with Linear and Nonlinear Tricks

IN THIS CHAPTER

Expanding your features 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 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 tackle the most difficult data problems in data science. However, neural networks and tree ensembles have overtaken SVMs ...