What this book covers

Chapter 1, Introducing Machine Learning with scikit-learn, is a brief introduction to the different types of machine learning and its applications.

Chapter 2, Predicting Categories with K-Nearest Neighbors, covers working with and implementing the k-nearest neighbors algorithm to solve classification problems in scikit-learn.

Chapter 3, Predicting Categories with Logistic Regression, explains the workings and implementation of the logistic regression algorithm when solving classification problems in scikit-learn.

Chapter 4, Predicting Categories with Naive Bayes and SVMs, explains the workings and implementation of the Naive Bayes and the Linear Support Vector Machines algorithms when solving classification problems in scikit-learn.

Chapter 5, Predicting Numeric Outcomes with Linear Regression, explains the workings and implementation of the linear regression algorithm when solving regression problems in scikit-learn.

Chapter 6, Classification and Regression with Trees, explains the workings and implementation of tree-based algorithms such as decision trees, random forests, and the boosting and ensemble algorithms when solving classification and regression problems in scikit-learn.

Chapter 7, Clustering Data with Unsupervised Machine Learning, explains the workings and implementation of the k-means algorithm when solving unsupervised problems in scikit-learn.

Chapter 8, Performance Evaluation Methods, contains visual performance evaluation techniques for supervised and unsupervised machine learning algorithms.