Chapter 1, Classification Using K-Nearest Neighbors, classifies a data item based on the most similar k items.
Chapter 2, Naive Bayes, delves into Bayes' Theorem with a view to computing the probability a data item belonging to a certain class.
Chapter 3, Decision Trees, organizes your decision criteria into the branches of a tree, and uses a decision tree to classify a data item into one of the classes at the leaf node.
Chapter 4, Random Forests, classifies a data item with an ensemble of decision trees to improve the accuracy of the algorithm by reducing the negative impact of the bias.
Chapter 5, Clustering into K Clusters, divides your data into k clusters to discover the patterns and similarities between the data items and goes into how to exploit these patterns to classify new data.
Chapter 6, Regression, models phenomena in your data by using a function that can predict the values of the unknown data in a simple way.
Chapter 7, Time-Series Analysis, unveils the trends and repeating patterns in time-dependent data to predict the future of the stock market, Bitcoin prices, and other time events.
Appendix A, Python Reference, is a reference of the basic Python language constructs, commands, and functions used throughout the book.
Appendix B, Statistics, provides a summary of the statistical methods and tools that are useful to a data scientist.
Appendix C, Glossary of Algorithms and Methods in Data Science, provides a glossary of some of the most important and powerful algorithms and methods from the fields of data science and machine learning.
