Outlier Detection Algorithms in Data Mining and Data Science

Outlier Detection in Data Mining, Data Science, Machine Learning, Data Analysis and Statistics using PYTHON,R and SAS

4.00 (217 reviews)
Udemy
platform
English
language
Data Science
category
instructor
2,197
students
2.5 hours
content
Jan 2019
last update
$39.99
regular price

What you will learn

This course brings you both theoretical and practical knowledge, starting with basic and advancing to more complex outlier algorithms

You can hone your programming skills because all algorithms you’ll learn have implementation in PYTHON, R and SAS

Description

Welcome to the course " Outlier Detection Techniques ". 

Are you Data Scientist or Analyst or maybe you are interested in fraud detection for credit cards, insurance or health care, intrusion detection for cyber-security, or military surveillance for enemy activities?

Welcome to Outlier Detection Techniques, a course designed to teach you not only how to recognise various techniques but also how to implement them correctly. 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’ll learn have implementation in PYTHON, R and SAS.

So what do you need to know before you get started? In short, not much! This course is perfect even for those with no knowledge of statistics and linear algebra.

Why wait? Start learning today! Because Everyone, who deals with the data,  needs to know  "Outlier Detection Techniques"!



The process of identifying outliers has many names in Data Mining and Machine learning such as outlier mining, outlier modeling, novelty detection or anomaly detection. Outlier detection algorithms are useful in areas such as: Data Mining, Machine LearningData Science, Pattern Recognition, Data Cleansing, Data Warehousing, Data Analysis, and Statistics.

I will present you on the one hand, very popular algorithms used in industry, but on the other hand, i will introduce you also new and advanced methods developed in recent years, coming from Data Mining.

You will learn algorithms for detection outliers in Univariate space, in Low-dimensional space and also learn innovative algorithm 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. So, in my teaching method, I put a stronger emphasis on understanding the material, and less on programming. However, anyone who interested in programming, I developed all algorithms in R , Python and SAS,  so you can download and run them.


List of Algorithms:

Univariate space:

1. Three Sigma Rule ( Statistics , R + Python + SAS programming languages)

2. MAD ( Statistics , R + Python + SAS programming languages )

3. Boxplot Rule ( Statistics , R + Python + SAS programming languages )

4. Adjusted Boxplot Rule ( Statistics , R + Python + SAS programming languages )

Low-dimensional Space :

5. Mahalanobis Rule ( Statistics , R + Python + SAS programming languages )

6. LOF - Local Outlier Factor ( Data Mining , R + Python + SAS programming languages)


High-dimensional Space:

7. ABOD - Angle-Based Outlier Detection ( Data Mining , R + Python + SAS programming languages)

I sincerely hope you will enjoy the course.


Content

Introduction

Introduction to Outlier Detection
Introduction to Outlier Detection
Mean, Median and Variance
Mean, Median and Variance

Detection Outliers in Univariate space

Three Sigma Rule
Three Sigma Rule
Masking and Swamping effects
Masking and Swamping effects
MAD Rule
MAD Rule
Boxplot Rule
Boxplot Rule
Adjusted Boxplot Rule
Adjusted Boxplot Rule

Detection Outliers in Multivariate space

Introduction to Linear Algebra, Part1
Introduction to Linear Algebra, Part1
Introduction to Linear Algebra, Part2
Introduction to Linear Algebra, Part2
Mahalanobis Rule
Mahalanobis Rule
LOF - Local Outlier Factor
LOF

Detection Outliers in High-Dimensional space

ABOD - Angle-Based Outlier Detection
ABOD

Final

Final Lecture

Screenshots

Outlier Detection Algorithms in Data Mining and Data Science - Screenshot_01Outlier Detection Algorithms in Data Mining and Data Science - Screenshot_02Outlier Detection Algorithms in Data Mining and Data Science - Screenshot_03Outlier Detection Algorithms in Data Mining and Data Science - Screenshot_04

Reviews

Shahad
February 24, 2021
Thank you very much for this information. But suddenlyI have problems with sound on this Lectures. I cant Hear the lecture on chrome browser
Lohit
February 8, 2021
Voice was not clear in any Section except in Section 1. Some practical examples in R/Python would really help!!
Saurabh
March 29, 2020
Poor Language content, barely understandable. Many important methods of OUtlier detection are missing.
Abhishek
June 14, 2018
yes so far, although would like more advanced techniques like isolation forest, HBOS, convex alpha hull
Rusdianto
June 7, 2018
Glad found this course to understand the theory behind the outliers detection. The course is short and to the point, plus KDD provides python code as basis for larger exercise. Thanks
Dawn
May 3, 2018
I learned a lot about the mathematical models associated with data mining and I view this course as a resource. Since I am a beginner, I still need additional education and I don't know what to learn next. Any suggestions (predictive analytics)?
Bill
April 5, 2018
I learned in great depth how outlier detection algorithms work. The illustrations were very helpful in understanding why specific data points were considered outliers by each algorithm. The one recommendation I would make for improving the course would be to try and include more real-world examples and data sets into the lectures. Overall, this is a very good course for learning the fundamentals.
Konrad
January 13, 2018
Good for knowing the methods of outlier detection but lacking practical examples of using packages in R or Python on sample data sets and data streams
Yacine
December 20, 2017
Good and well explained overview of the more common outlier detection techniques, with practice questions.
Fariss
December 17, 2017
This is a very comprehensive course with the ability to practice what you've learned. Excellent course!!!
Hans
December 15, 2017
I like this course very much. It has everything explained very simple and paying attention to details!
Tyler
December 5, 2017
This course covers just enough theory and practical approaches to the topic making it a perfect learning experience.
Ronald
November 18, 2017
Great course with clear explanations and techniques in an intuitive and practical manner. Sincerely appreciate the efforts of the instructor.
Jane
November 18, 2017
Can't wait to start using what is being teach here! Very well explained in clear language. Very pleased
Shubhashis
October 23, 2017
This course was really helpful to me. The contents are well-structured. The examples are easy to understand.

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1099670
udemy ID
2/2/2017
course created date
8/4/2020
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