Uncover hidden insights in your data with unsupervised learning. In Unsupervised Learning , you'll discover the power of clustering and dimensionality reduction techniques to identify hidden patterns and relationships in large, unlabelled datasets. This practical guide provides you with the tools to analyze complex data and extract valuable insights without needing predefined labels-perfect for anyone interested in learning how to apply unsupervised learning methods in the real world. Inside, you'll learn how to: ...
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Uncover hidden insights in your data with unsupervised learning. In Unsupervised Learning , you'll discover the power of clustering and dimensionality reduction techniques to identify hidden patterns and relationships in large, unlabelled datasets. This practical guide provides you with the tools to analyze complex data and extract valuable insights without needing predefined labels-perfect for anyone interested in learning how to apply unsupervised learning methods in the real world. Inside, you'll learn how to: Understand unsupervised learning : explore the core concepts of clustering and dimensionality reduction, and how they differ from supervised learning. Build clustering models (k-means, hierarchical clustering, DBSCAN) to group similar data points for tasks like customer segmentation, anomaly detection, and image grouping. Explore dimensionality reduction techniques like PCA (Principal Component Analysis) and t-SNE to reduce feature space and visualize complex data. Use k-means clustering to find natural groupings in unlabelled data and visualize those clusters in 2D or 3D space. Apply hierarchical clustering to discover the structure in your data and create dendrograms to visualize relationships. Implement density-based clustering with DBSCAN to detect irregular clusters and noise in your dataset. Evaluate clustering results using internal validation methods like silhouette score and Davies-Bouldin index. Apply dimensionality reduction for feature extraction, noise reduction, and efficient data visualization. Learn how to use t-SNE for visualizing high-dimensional data and uncovering hidden patterns. Work with real-world datasets : apply clustering and dimensionality reduction to marketing, customer behavior, and market basket analysis. Utilize Python libraries like scikit-learn , pandas , and matplotlib for implementing and visualizing unsupervised learning models. Packed with step-by-step tutorials , real-world examples , and hands-on exercises , this book is the key to mastering unsupervised learning techniques that reveal hidden insights from unlabelled data. Who This Book Is For Beginners looking to understand the fundamentals of unsupervised learning Data analysts and data scientists wanting to apply clustering and dimensionality reduction to uncover hidden patterns Developers seeking to build real-world applications using unsupervised learning techniques Students looking for a practical guide to learning and applying unsupervised learning methods Researchers and professionals who need to analyze complex, unlabelled datasets Uncover hidden patterns and make sense of your data with unsupervised learning techniques.
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Add this copy of Unsupervised Learning Discovering Hidden Patterns in to cart. £19.01, new condition, Sold by Ingram Customer Returns Center rated 5.0 out of 5 stars, ships from NV, USA, published 2025 by Independently Published.
