Learn how to build recommender systems and detect anomalies using modern ML techniques.
What You'll Learn
- Build user-item recommender models
- Apply collaborative & content-based filtering
- Implement anomaly detection algorithms
- Evaluate recommendations with precision, recall & NDCG
- Detect outliers using clustering, density & distance methods
Requirements
- Python
- pandas
- scikit-learn
- basic ML knowledge
Earn Your Certificate
Showcase your skills with a certificate! Complete the course, submit your project, and earn your certificate. Here's a sample of what you'll receive to show off your accomplishments.
Course Curriculum
4 Chapters
20 Lectures
20h 45m 18s
1. Fundamentals of Recommender & Anomaly Systems
5 Lectures|1 Assignment|1 Quiz|1h 39m
1. What Are Recommender & Anomaly Systems?
2. Types of Recommender Systems: CF & Content-Based
3. Basics of Anomaly Detection
4. Data Needs & Preprocessing
5. When To Use Which System?
6. Lesson 1 Fundamentals
7. Dataset Exploration & Summary
8. Fundamentals Reference Sheet
2. Recommender System Modeling
5 Lectures|1 Assignment|5h 59m
3. Anomaly Detection Algorithms
5 Lectures|1 Quiz|9h 10m 10s
4. Combined Systems & Real Projects
5 Lectures|1 Assignment|1 Quiz|3h 57m 8s
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$99.00
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