Chapter 1, Getting started with data mining, introduces the technologies we will be using, along with implementing two basic algorithms to get started.
Chapter 2, Classifying with scikit-learn, covers classification, a key form of data mining. You’ll also learn about some structures for making your data mining experimentation easier to perform..
Chapter 3, Predicting Sports Winners with Decisions Trees, introduces two new algorithms, Decision Trees and Random Forests, and uses it to predict sports winners by creating useful features..
Chapter 4, Recommending Movies using Affinity Analysis, looks at the problem of recommending products based on past experience, and introduces the Apriori algorithm.
Chapter 5, Features and scikit-learn Transformers, introduces more types of features you can create, and how to work with different datasets.
Chapter 6, Social Media Insight using Naive Bayes, uses the Naïve Bayes algorithm to automatically parse text-based information from the social media website Twitter.
Chapter 7, Follow Recommendations Using Graph Mining, applies cluster analysis and network analysis to find good people to follow on social media.
Chapter 8, Beating CAPTCHAs with Neural Networks, looks at extracting information from images, and then training neural networks to find words and letters in those images.
Chapter 9, Authorship attribution, looks at determining who wrote a given documents, by extracting text-based features and using Support Vector Machines.
Chapter 10, Clustering news articles, uses the k-means clustering algorithm to group together news articles based on their content.
Chapter 11, Object Detection in Images using Deep Neural Networks, determines what type of object is being shown in an image, by applying deep neural networks.
Chapter 12, Working with Big Data, looks at workflows for applying algorithms to big data and how to get insight from it.
Appendix, Next step, goes through each chapter, giving hints on where to go next for a deeper understanding of the concepts introduced.
