Extreme values or anomalies are present in almost any dataset, and it is critical to detect and deal with them before continuing statistical exploration. When left untouched, anomalies can easily disrupt your analyses and skew the performance of machine learning models.
In this course, you'll leverage Python to implement a variety of anomaly detection methods. You'll spot extreme values visually and use tested statistical techniques like Median Absolute Deviation for univariate datasets. For multivariate data, you'll learn to use estimators such as Isolation Forest, k-Nearest-Neighbors, and Local Outlier Factor. You'll also learn how to ensemble multiple outlier classifiers into a low-risk final estimator. You'll walk away with an essential data science tool in your belt: anomaly detection with Python.Lee mas.
Este recurso es ofrecido por un socio afiliado. Si paga por la capacitación, podemos ganar una comisión para respaldar este sitio.
Las técnicas y herramientas cubiertas en Anomaly Detection in Python son muy similares a los requisitos que se encuentran en los anuncios de trabajo de Científico de datos.
