Mastering Unlabeled Data Meap V05 Chapters 1 To 7 Of 11 Vaibhav Verdhan by Vaibhav Verdhan 9781617298721, 1617298727 instant download after payment.

Discover all-practical implementations of the key algorithms and models for handling unlabelled data. Full of case studies demonstrating how to apply each technique to real-world problems.
 
Models and Algorithms for Unlabeled Data introduces mathematical techniques, key algorithms, and Python implementations that will help you build machine learning models for unannotated data. You’ll master everything from kmeans and hierarchical clustering, to advanced neural networks like GANs and Restricted Boltzmann Machines. You’ll learn the business use case for different models, and master best practices for structured, text, and image data. Each new algorithm is introduced with a case study for retail, aviation, banking, and more—and you’ll develop a Python solution to fix each of these real-world problems. At the end of each chapter, you’ll find quizzes, practice datasets, and links to research papers to help you lock in what you’ve learned and expand your knowledge.
 
In Mastering Unlabeled Data 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
• Unsupervised time series clustering, Gaussian Mixture models, and statistical methods
• Building neural networks such as GANs and autoencoders
• How to interpret the results of unsupervised learning
• Choosing the right algorithm for your problem
• Deploying unsupervised learning to production
• Business use cases for machine learning and unsupervised learning