Among the four common classes of applications of AI: Anomaly detection detects data points in data that does not fit well with the rest of the data.
It has a wide range of applications such as fraud detection, surveillance, diagnosis, data cleanup, and predictive maintenance.
This Professional course explores what is anomaly system, different anomaly detection techniques, discusses the key idea behind those techniques, and wraps up with a discussion on how to make use of those results.
Ok enough of the theory dude! Register yourself to know more !
This training is best suited for IT, data management, and analytics professionals looking to gain expertise in AI and ML including: Software Developers and Architects, Analytics Professionals, Senior IT professionals, Testing and Mainframe Professionals, Data Management Professionals, Business Intelligence Professionals, Project Managers, Aspiring Data Scientists, Graduates looking to begin a career in Big Data Analytics and Data Science or AI
- You need to have intermediate to advanced Python experience. You are familiar with object-oriented programming. You can write nested for loops and can read and understand code written by others.
-Intermediate statistics background. You are familiar with probability.
-Intermediate knowledge of machine learning techniques. You can describe backpropagation, and have seen a few examples of neural network architecture (preferrably a recurrent neural network or a long short-term memory network).
-You have seen or worked with a deep learning framework like TensorFlow, Keras, or PyTorch before.
Spectrum of Anomaly detection techniques
Nearest Neighbour based Methods
Rule based Anomaly Detection
Classification based Models for Anomaly Detection