ONLEARN COURSE
Anomaly Detection for Real-World Systems
Uncover hidden patterns and critical outliers in complex datasets using advanced statistical and machine learning techniques. Protect your systems from unforeseen events.
IntermediateEnglish4 sections4.5 hours399 students
What you’ll be able to do
- Identify and categorize different types of anomalies in various datasets.
- Apply statistical methods (e.g., Z-score, IQR) for outlier detection.
- Implement machine learning algorithms (e.g., Isolation Forest, One-Class SVM) for unsupervised anomaly detection.
- Utilize time-series specific techniques (e.g., moving averages, exponential smoothing) to detect anomalies in sequential data.
- Preprocess and engineer features effectively for anomaly detection models.
- Evaluate the performance of anomaly detection models using appropriate metrics.
- Develop and deploy practical anomaly detection solutions using Python and relevant libraries.
- Interpret anomaly detection results and derive actionable insights for system monitoring.
Inside this course
4 sections · 20 activities · 4.5 hours total length
01Introduction to Anomaly Detection and Data Preparation
- What is Anomaly Detection?10:00
- Types of Anomalies8:00
- Data Preprocessing for Anomaly Detection20:00
- Feature Engineering for Anomaly Detection10:00
02Statistical and Distance-Based Methods
- Z-score and IQR for Outlier Detection25:00
- Gaussian Distribution and Probability Density Estimation15:00
- Local Outlier Factor (LOF)20:00
- Hands-on: Implementing LOF in Python20:00
- Quiz: Statistical and Distance-Based Methods5:00
03Machine Learning for Anomaly Detection
- Isolation Forest Algorithm15:00
- Implementing Isolation Forest25:00
- One-Class SVM for Novelty Detection15:00
- Hands-on: One-Class SVM Implementation20:00
- Introduction to Autoencoders for Anomaly Detection10:00
- Quiz: Machine Learning Models5:00
04Time-Series Anomaly Detection and Model Evaluation
- Basics of Time-Series Data for Anomaly Detection10:00
- Statistical Methods for Time-Series Anomalies20:00
- Evaluation Metrics for Anomaly Detection15:00
- Thresholding Strategies and Alerting10:00
- Project: Real-time System Monitoring Anomaly Detector25:00
About this course
Master practical anomaly detection techniques to safeguard your real-world systems. This course equips you with the skills to identify unusual patterns in data, preventing costly outages and ensuring system integrity.
Who this is for
- Data Scientists looking to specialize in anomaly detection.
- Machine Learning Engineers responsible for system monitoring and fraud detection.
- DevOps Engineers and SREs interested in proactive system health checks.
- Analysts who need to identify unusual patterns in business or operational data.
- Anyone interested in applying practical data science techniques to solve real-world problems.
Before you begin
- Intermediate Python programming skills (functions, classes, data structures).
- Basic understanding of data science concepts (e.g., data types, dataframes).
- Familiarity with NumPy and Pandas libraries.
- A computer with an internet connection and access to a Python environment (Jupyter Notebooks, Google Colab, or local setup).
LEARNING EXPERIENCES
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A few useful details
How will I learn?
Work through the course sections and activities in your Onlearn course player, at your own pace. Your progress is saved to your account.
Is this a subscription?
No. Each course is purchased separately with one payment.