In This Chapter
Expanding your feature using polynomials
Learning from big data
Using support vector machines
Previous chapters introduced 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; controlling the variance of estimates by regularization; and managing to learn 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. SVMs are able to perform the most difficult data problems and are a perfect substitute for neural networks such as the multilayer perceptron, which isn’t currently present in the Scikit-learn package ...