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Machine Learning
Unsupervised Learning
Discover how to extract meaningful patterns from unlabeled data using clustering and anomaly detection techniques. This course equips you with practical skills to identify hidden structures and outliers in datasets, enhancing your data analysis capabilities.
Contact usCourse Outcome
Master the k-means algorithm and Isolation Forest for effective data clustering and anomaly detection.
Duration
30 hours
Level
Beginner
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Key Program Takeaways
Clustering with K-Means
Learn to segment data into groups using the k-means algorithm, optimizing cluster selection for various applications.
Anomaly Detection Techniques
Understand and apply methods like Isolation Forest and k-nearest neighbors to identify outliers in datasets.
Practical Application
Gain hands-on experience by applying unsupervised learning techniques to real-world datasets, enhancing your analytical skills.
Modules Overview
6 Modules
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Module 1. Introduction
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Module 2. Entry Assessment
1.5h
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Module 3. Cluster Analysis
2h
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Module 4. Anomaly Detection
1h
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Module 5. Final Assessment
1.5h
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Module 6. Conclusion
Our Experts
Our instructors are industry professionals and machine learning practitioners with real-world experience developing AI solutions for a variety of sectors. Their mission is to guide you through your first steps into the world of ML with clarity, support, and practical insights.
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