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Complete Outlier Detection Algorithms A-Z: In Data Science

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Course Created By,  Saurav Singla

All Level 1 Hour 40.12 mins of Video Lecture Language: English Lifetime Access Certificate of completion Flash Sale $19 $67 Discount: 71.64% off
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Key Topics Covered
  • Understand the fundamentals of Outliers
  • You will learn outlier algorithms used in Data Science, Machine Learning with Python Programming
  • You will learn both theoretical and practical knowledge, starting with basic to complex outlier algorithms
  • You will learn approaches to modelling outliers / anomaly detection
  • Determine how to apply a supervised learning algorithm to a classification problem for outlier detection
  • Apply and assess a nearest-neighbor algorithm for identifying anomalies in the absence of labels
  • Apply a supervised learning algorithm to a classification problem for anomaly and outlier detection
  • Make judgments about which methods among a diverse set work best to identify anomalies

Who can take this course?
  • Data Scientist or Data Analyst or Financial Analyst or Business Analyst or Software Engineers or Technical Managers
  • People interested in outlier detection, anomality detection, fraud detection, unseen pattern in data
  • People who want a career in Data Science or Data Analytics
  • Familiarity with the Python is needed since support for Python in the tutorial is limited
  • You should be familiar with basic supervised and unsupervised learning techniques

Course Description

Welcome to the course "Complete Outlier Detection Algorithms A-Z: In Data Science". This is the most comprehensive, yet straight-forward, course for the outlier detection! Are you Data Scientist or Data Analyst or Financial Analyst or maybe you are interested in anomaly detection or fraud detecti... Read More

Welcome to the course "Complete Outlier Detection Algorithms A-Z: In Data Science". This is the most comprehensive, yet straight-forward, course for the outlier detection! Are you Data Scientist or Data Analyst or Financial Analyst or maybe you are interested in anomaly detection or fraud detection? The course is designed to teach you the various techniques which can be used to identify and recognize outliers in any set of data. The process of identifying outliers has many names in Data Science and Machine learning such as outlier modeling, novelty detection, or anomaly detection. Outlier detection algorithms are useful in areas such as Machine Learning, Deep Learning, Data Science, Pattern Recognition, Data Analysis, and Statistics. I will present to you very popular algorithms used in the industry as well as advanced methods developed in recent years, coming from Data Science.
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