دانلود کتاب Mastering Unlabeled Data - MEAP V06

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
• 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, and Flask
• How to interpret the results of unsupervised learning
• Choosing the right algorithm for your problem