Become an Expert in Anomaly Detection and Outlier Analytics

This isn’t just a course; it’s a project-based certification designed to make you an expert in Anomaly Detection & Outlier Analytics. Mastering anomaly detection is crucial for stopping fraud, securing systems against intrusions, and enabling precise predictive maintenance.

We move far beyond basic statistics straight into state-of-the-art machine learning. The core of this program is practical application using Python, Scikit-learn, and specialized libraries like PyOD. You’ll tackle real-world case studies, including credit card fraud and industrial equipment failure prediction using actual datasets. This hands-on approach ensures you gain skills immediately applicable in the industry.

The curriculum systematically covers supervised, unsupervised, and semi-supervised techniques. You’ll dive deep into essential algorithms like Isolation Forest (iForest), Local Outlier Factor (LOF), and One-Class SVM (OC-SVM). We also cover advanced methods for time series data, including deep learning approaches. Emphasizing proper data preparation and feature engineering is vital for model success. Upon completion, you won’t just know the concepts; you’ll be ready for production-level deployment, proficient in model building, result interpretation, and expertly handling the tough challenge of class imbalance inherent in outlier problems. This expertise will make you a highly sought-after specialist in any data science team. Get certified and transform your career.

What you will learn:

• Understand the principles of anomaly detection and outlier analytics
• Utilize Python and machine learning libraries to implement solutions
• Apply techniques to real-world cases like credit card fraud and predictive maintenance

Course Content:

• Sections: 15
• Lectures: 50
• Duration: 12 hours

Requirements:

• Basic knowledge of Python
• Understanding of machine learning concepts

Who is it for?

• Data Scientists looking to specialize in fraud detection, cybersecurity, or industrial predictive maintenance.
• Machine Learning Engineers responsible for monitoring system health and identifying system failures.
• Risk Management Professionals requiring advanced statistical and ML tools for outlier analysis.

What are you waiting for to get started?

Enroll today and take your skills to the next level. Coupons are limited and may expire at any time!

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