Formed in 2009, the Archive Team (not to be confused with the archive.org Archive-It Team) is a rogue archivist collective dedicated to saving copies of rapidly dying or deleted websites for the sake of history and digital heritage. The group is 100% composed of volunteers and interested parties, and has expanded into a large amount of related projects for saving online and digital history.
Book Description
Unsupervised learning is useful and practical in situations where labelled data is not available. Unsupervised Learning with Python shows you the best practices for using unsupervised learning techniques in tandem with Python libraries and extracting meaningful information from unstructured data.
The book begins by explaining how basic clustering works to find similar data points in a set. You will learn in detail various clustering methods, such as K-means, hierarchical clustering, and DBSCAN, and build algorithms from scratch using these methods. Then, you will learn about dimensionality reduction and its applications. You will also learn Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE) and Autoencoders in detail, and learn their implementations and their shortcomings. You will use sklearn to implement and analyse PCA on the Iris dataset. Then, you will use Keras to build autoencoder models for the CIFAR 10 dataset and visualize them using t-SNE. While studying the applications of unsupervised learning, you will explore mine trending topics in twitter or facebook, and build a news recommendation engine for readers.
You will complete the book with several interesting activities, such as performing a market basket analysis and finding relations between different merchandises, using hotspot discovery and KDE algorithm to analyze crime data in London for this effort, and using the Apriori algorithm to study transaction data.
