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Acupressure - Prakritik Upchar: Discover the Power of DIY Healing with Dr. Attar Singh's Book
With simple techniques and easy-to-follow instructions, this book empowers individuals to cure numerous ailments by simply applying pressure to specific acupressure points on the hands and feet. Featuring vivid diagrams charts for enhanced understanding, Dr. Attar Singh's.
Buy Veda Set of 8 Books: Sam, Rig, Yajur & Atharva Ved Sanhita by Pandit Shriram Sharma Acharya
The Veda Set of 8 Books by Pandit Shriram Sharma Acharya is a comprehensive collection that brings together the four major Vedas—Rigveda, Samaveda, Yajurveda, and Atharvaveda—in their original Samhitas (the foundational texts of Vedic knowledge).
Pandit Shriram Sharma Acharya, one of the most revered spiritual and scholarly figures of the 20th century, meticulously compiled and translated these ancient texts for contemporary readers, preserving their purity and essence while offering deep insights into their relevance today. The Veda Set of 8 Books serves as an invaluable resource for scholars, spiritual seekers, and anyone interested in exploring the rich heritage of Vedic knowledge.
International Petrochemical Scenario, Petrochemical from Refinery, Feedstock and Petroleum Industry, Petrochemicals from Fluidized Catalytic Cracking Process
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Maximize your container workload efficiency with DevOps practices in Kubernetes & Learn how to build, deploy, use, & maintain your applications on Kubernetes.
Description
Creating data models has never been straightforward. This book demonstrates how to formulate a complete business analytics model that combines several data sources, executes numerous computations, and scales across hundreds of BI users.To begin, you'll learn about the Microsoft Power BI ecosystem by downloading the Power BI desktop and exploring all of its features and capabilities.
The book will be divided into chapters based on primary Machine Learning topics like Classification, Regression, Clustering, Deep Learning, Text Mining, etc. The book will also explain different techniques of putting Machine Learning models into production-grade systems using Big Data or Non-Big Data flavors and standards for exporting models.