Complete Outlier Detection Algorithms A-Z: In Data Science

Udemy Complete Outlier Detection Algorithms A-Z: In Data Science

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Outlier Detection Algorithms in Data Science, Machine Learning, Deep Learning, Data Analysis, Statistics with Python

What you'll learn
  • 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

  • It is assumed that you have completed and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, Matplotlib, Scikit-learn
  • 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

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 on UDEMY!
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. You will learn algorithms for detection outliers in Univariate space, in Low-dimensional space and also learn the innovative algorithms for detection 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. Anyone who 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 Based 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! Because 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 or 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.
Who this course is for:
  • 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
  • This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on outlier detection and anomality detection
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