Hands-On Unsupervised Learning with Python: Implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more

Description

Unsupervised learning is an increasingly important branch of data science, the goal of which is to train models that can learn the structure of a dataset and provide the user with helpful pieces of information about new samples. In many different business sectors (such as marketing, business intelligence, strategy, and so forth), unsupervised learning has always had a primary role in helping the manager to make the best decisions, based both on qualitative and, above all, quantitative approaches. In a world where data is becoming more and more pervasive and storage costs are dropping, the possibility of analyzing real, complex datasets is helping to transform old-fashioned business models into new, more accurate, more responsive, and more effective ones. That's why a data scientist might not have a clear idea about all the possibilities, focusing on the pros and cons of all methods and increasing their knowledge about the best potential strategies for every specific domain. This book is not intended to be an exhaustive resource (which is actually impossible to find), but more of a reference to set you off on your exploration of this world, providing you with different methods that can be immediately employed and evaluated. I hope that readers with different backgrounds will learn worthwhile things for improving their businesses, and that you'll seek more study of this fascinating topic! Title Page Copyright and Credits Hands-On Unsupervised Learning with Python About Packt Why subscribe? Packt.com Contributors About the author About the reviewer Packt is searching for authors like you Preface Who this book is for What this book covers To get the most out of this book Download the example code files Download the color images Conventions used Get in touch Reviews Getting Started with Unsupervised Learning Technical requirements Why do we need machine learning? Descriptive analysis Diagnostic analysis Predictive analysis Prescriptive analysis Types of machine learning algorithm Supervised learning algorithms Supervised hello world! Unsupervised learning algorithms Cluster analysis Generative models Association rules Unsupervised hello world! Semi-supervised learning algorithms Reinforcement learning algorithms Why Python for data science and machine learning? Summary Questions Further reading Clustering Fundamentals Technical requirements Introduction to clustering Distance functions K-means K-means++ Analysis of the Breast Cancer Wisconsin dataset Evaluation metrics Minimizing the inertia Silhouette score Completeness score Homogeneity score A trade-off between homogeneity and completeness using the V-measure Adjusted Mutual Information (AMI) score Adjusted Rand score Contingency matrix K-Nearest Neighbors Vector Quantization Summary Questions Further reading Advanced Clustering Technical requirements Spectral clustering Mean shift DBSCAN Calinski-Harabasz score Analysis of the Absenteeism at Work dataset using DBSCAN Cluster instability as a performance metric K-medoids Online clustering Mini-batch K-means BIRCH Comparison between mini-batch K-means and BIRCH Summary Questions Further reading Hierarchical Clustering in Action Technical requirements Cluster hierarchies Agglomerative clustering Single and complete linkages Average linkage Ward's linkage Analyzing a dendrogram Cophenetic correlation as a performance metric Agglomerative clustering on the Water Treatment Plant dataset Connectivity constraints Summary Questions Further reading Soft Clustering and Gaussian Mixture Models Technical requirements Soft clustering Fuzzy c-means Gaussian mixture EM algorithm for Gaussian mixtures Assessing the performance of a Gaussian mixture with AIC and BIC Component selection using Bayesian Gaussian mixture Generative Gaussian mixture Summary Questions Further reading Anomaly Detection Technical requirements Probability density functions Anomalies as outliers or novelties Structure of the dataset Histograms Kernel density estimation (KDE) Gaussian kernel Epanechnikov kernel Exponential kernel Uniform (or Tophat) kernel Estimating the density Anomaly detection Anomaly detection with the KDD Cup 99 dataset One-class support vector machines Anomaly detection with Isolation Forests Summary Questions Further reading Dimensionality Reduction and Component Analysis Technical requirements Principal Component Analysis (PCA) PCA with Singular Value Decomposition Whitening PCA with the MNIST dataset Kernel PCA Adding more robustness to heteroscedastic noise with factor analysis Sparse PCA and dictionary learning Non-Negative Matrix Factorization Independent Component Analysis Topic modeling with Latent Dirichlet Allocation Summary Questions Further reading Unsupervised Neural Network Models Technical requirements Autoencoders Example of a deep convolutional autoencoder Denoising autoencoders Adding noise to the deep convolutional autoencoder Sparse autoencoders Adding a sparseness constraint to the deep convolutional autoencoder Variational autoencoders Example of a deep convolutional variational autoencoder Hebbian-based principal component analysis Sanger's network An example of Sanger's network Rubner-Tavan's network An example of a Rubner-Tavan's network Unsupervised deep belief networks Restricted Boltzmann Machines Deep belief networks Example of an unsupervised DBN Summary Questions Further reading Generative Adversarial Networks and SOMs Technical requirements Generative adversarial networks Analyzing a GAN Mode collapse Example of a deep convolutional GAN Wasserstein GANs Transforming the DCGAN into a WGAN Self-organizing maps Example of a Kohonen map Summary Questions Further reading Assessments Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 Other Books You May Enjoy Leave a review - let other readers know what you think