Series

Sianna Sherman – The Five Great Elements Yoga Series

Sianna Sherman – The Five Great Elements Yoga Series
English | Size: 4.67 GB
Category: Tutorial


A Deep Dive into the Alchemy of the Elements
~ Earth, Water, Fire, Air & Ether
The Five Great Elements with Sianna Sherman honors the alchemy of the elements, which the yoga tradition refers to as the panchamahabhutas. This level 4, 5-class Rasa Yoga program dives into The Five Great Elements: Earth, Water, Fire, Air & Ether. Each element offers empowerments for practice, life, and evolutionary growth. These 70-minute practices offer an alchemical approach to yoga with asana, mantra, mudra, pranayama, patterns of movement, and functional & subtle body anatomy. Learn the empowerments of each element, the characteristics, associations, and shadow aspects, too. Each practice is a dynamic embodiment of the element. Rasa Yoga is Sianna Sherman’s synthesis of yoga; a bhakti fusion of asana, mantra, mudra, myth, pranayama, meditation, functional anatomy, Tantric yoga philosophy, shadow work, and soul alchemy.

PluralSight – Time Series Forecasting with Amazon Forecast

PluralSight – Time Series Forecasting with Amazon Forecast Bookware-KNiSO
English | Size: 88.12 MB
Category: Tutorial


Amazon Forecast is a managed service that provides accurate future forecasts using machine learning. In this course, Time Series Forecasting with Amazon Forecast, you will learn to use Amazon Forecast and machine learning technology to create an accurate time series forecast. First, you will explore the basics of Amazon Forecast, how it works, and how to set it up to be ready for use. Next, you will explore how to create a target time series dataset, by creating a group dataset and understanding domains and dataset types. Next you will learn how to train a predictor, creating and deploying predictor queries, and obtaining and visualizing actual and predicted results in the console. When finished with this course, you will have the skills and knowledge on how to use Amazon Forecast to create accurate future time series forecasts by using custom model predictors with machine learning algorithms to create forecasts that can be easily reviewed