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 Mastering Unlabeled Data
Mastering Unlabeled Data
- 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 kmeans, 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 sklearn, bumpy, Pandas, matplotlib, Seaborn, Keras, TensorFlow, andFflask
- How to interpret the results of unsupervised learning
- Choosing the right algorithm for your problem
- Deploying unsupervised learning to production
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.