Duration
Anomaly Detection using Machine Learning and Deep Learning
1 day
What you'll learn
- What is an anomaly?
- What are the areas where anomaly detection can be applied?
- What are the three types of anomaly detection techniques?
- How to analyze time based data for anomalies?
- How to use supervised learning to identify anomalies?
- How to apply unsupervised learning algorithms like DBSCAN and Isolation Forest to detect anomalies?
- How to analyze images and identify anomalies among them?
Outline
Machine Learning
- Machine Learning Concepts
- Logistic Regression and Introduction to Deep Learning
- Unsupervised Learning
Types of Anomalies
- Understanding Anomalies
- Application Scenarios
- Anomaly Vs Outlier: Is Every Anomaly An Outlier?
Anomaly Detection - Time Series
- Understanding Anomaly Detection in Time Series Data
- Programming for Anomaly Detection - Time Series
Anomaly Detection - Unsupervised DBSCAN and Isolation Forest
- Anomaly Detection - Unsupervised DBSCAN
- Uncovering Isolation Forest Algorithm
- Isolation Forest Programming
- Explainer AI: Understand the Output of Algorithm
Anomaly Detection - Supervised
Anomaly Detection – Images and Using Deep Learning
- Intro and Background
- Anomaly Detection - Images
- GAN
- RBM, DBN
- Auto Encoder
- Building the anomaly detection models
PyOD: A comparison of 10 algorithms
Predicting High Impact Low Volume Events: Predictive Maintenance
Programme scope, delivery arrangements and suitability are confirmed during enquiry.