Chapter 1, Classification Using K Nearest Neighbors, Classify a data item based on the k most similar items.
Chapter 2, Naive Bayes, Learn Bayes Theorem to compute the probability a data item belonging to a certain class.
Chapter 3, Decision Trees, Organize your decision criteria into the branches of a tree and use a decision tree to classify a data item into one of the classes at the leaf node.
Chapter 4, Random Forest, Classify 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, Divide your data into k clusters to discover the patterns and similarities between the data items. Exploit these patterns to classify new data.
Chapter 6, Regression, Model a phenomena in your data by a function that can predict the values for the unknown data in a simple way.
Chapter 7, Time Series Analysis, Unveil the trend and repeating patters in time dependent data to predict the future of the stock market, Bitcoin prices and other time events.
Appendix A, Statistics, Provides a summary of the statistical methods and tools useful to a data scientist.
Appendix B, R Reference, Reference to the basic Python language constructs.
Appendix C, Python Reference, Reference to the basic R language constructs, commands and functions used throughout the book.
Appendix D, Glossary of Algorithms and Methods in Data Science, Provides a glossary for some of the most important and powerful algorithms and methods from the fields of the data science and machine learning.