Complete Outlier Detection Algorithms A-Z: In Data Science

person icon SAURAV SINGLA

Complete Outlier Detection Algorithms A-Z: In Data Science

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

updated on icon Updated on Sep, 2023

language icon Language - English

person icon SAURAV SINGLA

architecture icon Data Science,Algorithms


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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 on TutorialsPoint!

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. For 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 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.


What will you learn in this course:

  • 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


What are the prerequisites for this course?

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


Check out the detailed breakdown of what’s inside the course

11 Lectures
  • play icon Introduction of outlier 07:30 07:30
  • play icon Application of outlier detection 06:39 06:39
  • play icon Cause and impact of outlier 02:36 02:36
  • play icon Type of outliers 08:42 08:42
  • play icon Methods for Outlier detection 03:18 03:18
  • play icon Outlier detection in univariate 04:10 04:10
  • play icon Outlier detection in multivariate 20:11 20:11
  • play icon Outlier detection in high dimension 04:04 04:04
  • play icon Outlier detection in deep learning 05:02 05:02
  • play icon Best practices of outlier detection 02:01 02:01
  • play icon How to remove outliers? 04:43 04:43
7 Lectures

Instructor Details



Senior Data Scientist | Machine Learning | Mentor | Instructor | Speaker

I am a Senior Data Scientist, a Machine Learning Expert, a Data Science Course Instructor, a Mentor, a Speaker, a Data Science Subject Writer, a Podcaster.

Self-directed experienced data scientist with comprehensive accomplishments applying statistical modeling, machine learning, predictive modeling, natural language processing, deep learning, and data analytics to ensure success, and achieve goals with extensive use of Python, R, SQL & Tableau. Strong consulting industry acumens.

Demonstrated success in developing and seamlessly executing plans in complex organizational structures. Recognized for maximizing performance by implementing appropriate project management through analysis of details to ensure quality control and understanding of emerging technology.

I am a leader in capability building for data science, leading teams to excel in providing business value with the latest in technology.

I enjoy:
• Machine Learning systems to help customers and deliver results
• Engaging with business to define problems, deliverables, and outcomes
• Mentoring data practitioners to build high-performing teams and grow the industry
• Writing about effective data science, learning, and career
• Speaking at meetups about data science, and career
• Creating a data science course on Udemy

Data Analysis, Machine Learning, Statistical Modeling, Data Visualisation, Predictive Modeling, Prescriptive Modeling, Cognitive Modeling, Analysis, Business Intelligence, Business Analytics, parametric modeling, nonparametric modeling, Agent-based Modeling, System Dynamics, Discrete Event Simulation, Natural Language Processing, Deep Learning.

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