Data Without Labels, Video Edition [Update 05/2025]
English | Size: 1.6 GB
Genre: eLearning[/center]

In Video Editions the narrator reads the book while the content, figures, code listings, diagrams, and text appear on the screen. Like an audiobook that you can also watch as a video.

Discover all-practical implementations of the key algorithms and models for handling unlabeled data. Full of case studies demonstrating how to apply each technique to real-world problems.

In Data Without Labels you'll learn:

Fundamental building blocks and concepts of machine learning and unsupervised learning
Data cleaning for structured and unstructured data like text and images
Clustering algorithms like K-means, hierarchical clustering, DBSCAN, Gaussian Mixture Models, and Spectral clustering
Dimensionality reduction methods like Principal Component Analysis (PCA), SVD, Multidimensional scaling, and t-SNE
Association rule algorithms like aPriori, ECLAT, SPADE
Unsupervised time series clustering, Gaussian Mixture models, and statistical methods
Building neural networks such as GANs and autoencoders
Dimensionality reduction methods like Principal Component Analysis and multidimensional scaling
Association rule algorithms like aPriori, ECLAT, and SPADE
Working with Python tools and libraries like sci-kit learn, numpy, Pandas, matplotlib, Seaborn, Keras, TensorFlow, and Flask
How to interpret the results of unsupervised learning
Choosing the right algorithm for your problem
Deploying unsupervised learning to production
Maintenance and refresh of an ML solution

Data Without Labels introduces mathematical techniques, key algorithms, and Python implementations that will help you build machine learning models for unannotated data. You'll discover hands-off and unsupervised machine learning approaches that can still untangle raw, real-world datasets and support sound strategic decisions for your business.

Don't get bogged down in theory-the book bridges the gap between complex math and practical Python implementations, covering end-to-end model development all the way through to production deployment. You'll discover the business use cases for machine learning and unsupervised learning, and access insightful research papers to complete your knowledge.

About the Technology
Generative AI, predictive algorithms, fraud detection, and many other analysis tasks rely on cheap and plentiful unlabeled data. Machine learning on data without labels-or unsupervised learning-turns raw text, images, and numbers into insights about your customers, accurate computer vision, and high-quality datasets for training AI models. This book will show you how.

About the Book
Data Without Labels is a comprehensive guide to unsupervised learning, offering a deep dive into its mathematical foundations, algorithms, and practical applications. It presents practical examples from retail, aviation, and banking using fully annotated Python code. You'll explore core techniques like clustering and dimensionality reduction along with advanced topics like autoencoders and GANs. As you go, you'll learn where to apply unsupervised learning in business applications and discover how to develop your own machine learning models end-to-end.

What's Inside
Master unsupervised learning algorithms
Real-world business applications
Curate AI training datasets
Explore autoencoders and GANs applications

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