Complete Outlier Detection Algorithms Course: In Data Science

Welcome to the course "Complete Outlier Detection Algorithms A-Z: In Data Science".

This is the most comprehensive, yet straightforward, course for outlier detection on TutorialsPoint!

Are you a Data Scientist 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 that 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 modelling, 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. You will learn algorithms for the detection of outliers in Univariate space, and in Low-dimensional space and also learn the innovative algorithms for the detection of outliers in High-dimensional space.

I am convinced that only those who are familiar with the details of the methodology and know all the stages of the calculation can understand it in depth. For anyone who is interested in programming, I developed all algorithms in PYTHON, so you can download and run them.

List of Algorithms:

• Interquartile Range Method (IQR), Standard Deviation Method.

• KNN, DBSCAN, Local Outlier Factor, Clustering Local Outlier Factor, Isolation Forest, Minimum Covariance Determinant, One-Class SVM, Histogram-Based Outlier Detection, Feature Bagging, Local Correlation Integral.

• Angular Based Outlier Detection.

• Autoencoders.

Why wait? Start learning today! Everyone, who deals with the data, needs to know ‘Complete Outlier Detection Algorithms A-Z: In Data Science’, a necessity to recognize fraudulent transactions in the data set. No matter what you need outlier detection for, this course brings you both theoretical and practical knowledge, starting with basic and advancing to more complex algorithms. You can even hone your programming skills because all algorithms you will learn have an implementation in PYTHON. You will learn how to examine data with the goal of detecting anomalies or abnormal instances of outlier data points.

For the code explained in the tutorials, you can find a GitHub repository hyperlink.

At the end of this course, you will have understood the different aspects that affect how this problem can be formulated, the techniques applicable for each formulation, and knowledge of some real-world applications in which they are most effective.