unsupervised learning is one of the trendiest topics in ai today the book' goal is to outline the concepts and tools required for you to develop the intuition necessary for applying this technology to everyday problems that you work on in other wordsthis is an applied bookone that will allow you to build real-world systems we will also explore how to efficiently label unlabeled datasets to turn unsupervised learning problems into semisupervised ones the book will use hands-on approachintroducing some theory but focusing mostly on applying unsupervised learning techniques to solving real-world problems the datasets and code are available online as jupyter notebooks on github (bit ly/ gd earmed with the conceptual understanding and hands-on experience you'll gain from this bookyou will be able to apply unsupervised learning to largeunlabeled datasets to uncover hidden patternsobtain deeper business insightdetect anomaliescluster groups based on similarityperform automatic feature engineering and selectiongenerate synthetic datasetsand more prerequisites this book assumes that you have some python programming experienceincluding familiarity with numpy and pandas for more on pythonvisit the official python website (more on jupyter notebookvisit the official jupyter website (index htmlfor refresher on college-level calculuslinear algebraprobabilityand statisticsread part of the deep learning textbook (book org/by ian goodfellow and yoshua bengio for refresher on machine learningread the elements of statistical learning (roadmap the book is organized into four partscovering the following topicspart ifundamentals of unsupervised learning differences between supervised and unsupervised learningan overview of popular supervised and unsupervised algorithmsand an end-to-end machine learning project part iiunsupervised learning using scikit-learn dimensionality reductionanomaly detectionand clustering and group segmentation xvi preface