Anomaly Detection in Enterprises
Designed to elevate your expertise in detecting outliers across various domains, this course offers an integrated perspective on anomaly detection, emphasizing the synergy between methods from different communities, making it ideal for computer science professionals and data enthusiasts. We delve into practical examples to illustrate the significance of anomaly detection in diverse use cases like Intrusion Detection Systems, Credit Card Fraud, Sensor Networks, Medical Diagnosis, Law Enforcement and Earth Science
Original price was: ₹34,999.00.₹5,499.00Current price is: ₹5,499.00.
(Including GST)
Level: Intermediate
Scholarships: Available
Certification Available
Duration: 5 weeks
Capstone Project
Should you take this course?
Entrepreneurs (Tech/ Non-Tech)
Software Engineers
Mid-level Managers in Tech/ Non-Tech companies
AI / Deep Learning Students
Al enthusiasts/ Researchers
Tools Covered
What will we teach you?
- Probabilistic Models for Outlier Detection (Angle-Based Outlier Detection, The Mahalanobis method, Expectation Maximization)
- Linear Models (Linear Regression, PCA, One-Class SVM)
- Proximity-Based Outlier Detection (Distance based methods, ODIN: Reverse Nearest Neighbor Approach, Local Outlier Factor (LOF), Local Correlation Integral (LOCI), Histogram-Based techniques)
- High Dimensional Outlier Detection (Genetic Algorithms, Feature Bagging, Isolation Forests)
- Time Series and Streaming Outlier Detection
- Case studies on applications like quality control, fault detection, financial outlier detection, social media analysis and earth science applications
- You will learn to apply models for each of the above mentioned use case on a cloud platform and deploy it to an end point using Python Libraries like sklearn and PyOD
Probabilistic Models for Outlier Detection (Angle-Based Outlier Detection, The Mahalanobis method, Expectation Maximization)
Linear Models (Linear Regression, PCA, One-Class SVM)
Proximity-Based Outlier Detection (Distance based methods, ODIN: Reverse Nearest Neighbor Approach, Local Outlier Factor (LOF), Local Correlation Integral (LOCI), Histogram-Based techniques)
High Dimensional Outlier Detection (Genetic Algorithms, Feature Bagging, Isolation Forests)
Time Series and Streaming Outlier Detection
Case studies on applications like quality control, fault detection, financial outlier detection, social media analysis and earth science applications
You will learn to apply models for each of the above mentioned use case on a cloud platform and deploy it to an end point using Python Libraries like sklearn and PyOD
Enhance your expertise
01
Gain expertise through hands-on projects that delve into diverse use-cases across various industries
02
Learn through real-world case studies, gain practical expertise, and create a portfolio of projects
03
Evaluate and enhance your understanding through engaging quizzes and assignments
04
Live mentorship for career success from industry experts
05
Enhance online presence with personalized resume and LinkedIn reviews
06
Earn certification that validates your proficiency in Anomaly Detection and showcases your commitment to excellence
New to Anomaly Detection?
FAQs
What are the prerequisites for optimal course comprehension?
Learners should have basic proficiency in a programming language (e.g., C, Java, Python) and basic understanding of statistics and machine learning concepts is recommended for optimal learning. If you are new to Data Science, our Applied Data Science in Enterprises Certification is ideal. It focuses on creating AI and ML solutions tailored to business challenges and user objectives.
What skills will I gain from completing this certification program?
Enhance your programming and analytical skills, prepare for technical interviews, and master anomaly detection techniques using both coding and no-code tools. This course enables you to explore advanced topics in anomaly detection comprehensively.