Update README.md

Author: LiYangHart

README.md

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 # AutoML-Implementation-for-Static-and-Dynamic-Data-Analytics
 
-This code provides an **Automated Machine Learning (AutoML)** implementation for static and dynamic data analytics problems. It provides a case study of IoT anomaly detection using many ML algorithms and optimization/AutoML methods (for automating and optimizing ML algorithms). It can also be used as a **tutorial** to help machine learning researchers to automatically obtain optimized machine learning models with the optimal learning performance on any specific task.
+This code provides an **Automated Machine Learning (AutoML)** implementation for static and dynamic data analytics problems. It provides a case study of IoT anomaly detection using many ML algorithms and optimization/AutoML methods (for automating and optimizing ML algorithms). It involves the automation of all important procedures in the machine learning/data analytics pipeline, including automated data pre-processing, automated feature engineering, automated model selection, Hyper-Parameter Optimization (HPO), and automated model updating (model drift adaptation). It can also be used as a **tutorial** to help machine learning researchers to automatically obtain optimized machine learning models with the optimal learning performance on any specific task.
 - **Batch/Static Learning**: Batch learning is the traditional machine learning and data analytics process. Batch learning methods analyze static IoT data in batches and often need access to the entire dataset prior to model training.
 - **Online/Continual learning**: Online learning or continual learning techniques are able to train models using continuously incoming online data streams in dynamic IoT environments and address concept drift issues (data distribution changes).