“Hands-On Unsupervised Learning with Python” is your manual for the methods that enable machines to learn from the data not classified by an output label and discover patterns. Let’s begin with the distinction between discrete and continuous classification problems, the core concepts of supervised and unsupervised learning, and the optional section with essential Python libraries to solve these problems. With this knowledge, you can apply unsupervised learning in some real-life situations.
Chapters in the book discuss the algorithms and methods of clustering, as well as the selection of features and transformations so that you get an understanding of big data. You will also learn about more sophisticated types of clustering, learn how to perform a method known as principal components analysis, and learn how to construct an outlier detection model. The scenarios discussed below will help you implement these methodologies clearly and systematically, thus easing your understanding of these concepts and ideas.
In this book titled “Hands-On Unsupervised Learning with Python,” you will be able to build your very own neural network models and explore the newest advancement in technology, including GANs, in dealing with images by the end of this book. A beginning, intermediate, or advanced machine learner can benefit from this book by acquiring the tools and knowledge needed to conquer unsupervised learning tasks.
Hands-On Unsupervised Learning with Python Table of Contents:
- Getting Started with Unsupervised Learning
- Clustering Fundamentals
- Advanced Clustering
- Hierarchical Clustering in Action
- Soft Clustering and Gaussian Mixture Models
- Anomaly Detection
- Dimensionality Reduction and Component Analysis
- Unsupervised Neural Network Models
- Generative Adversarial Networks and SOMs
Who is this course for?
- Statisticians
- Data scientists
- Machine learning developers
- Deep learning practitioners
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