The tutorial is organized in a sequence of three sections: Introduction, Theory and Hands-on-demo. In part one, we will briefly discuss foundations of time series dataset with the help of real-world examples. We will also present a broad taxonomy of time series dataset. We will also present general definition of anomalies in time series data and discuss three common variants of Anomaly/Outlier Detection problems. Next, we discuss basic machine learning primitives such as Estimator, Transformer, Data Stationarizer, etc that are useful for building anomaly pipeline. In machine learning field, these components become a backbone for building a complex model learning pipelines. We will formally introduce the key API such as ``fit'', ``predict'', ``decision\_function'', to the participant with the help of 30+ different anomaly detection algorithms. Apart from provide the categorization of these algorithms, we will also discuss one algorithm namely Gaussian Graphical Model for interpretable anomaly detection. The access to the toolkit is made available via IBM API Hub Platform (https://developer.ibm.com/apis/catalog/ai4industry--anomaly-detection-product/Introduction). The example notebooks are accessible at IBM's public github (https://github.com/IBM/anomaly-detection-code-pattern/). The tutorial finally analyzes open issues and future directions in this vibrant and rapidly evolving research area.