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Outlier Detection Algorithms in Data Mining and Data Science

#artificialintelligence

Outlier Detection in Data Mining, Data Science, Machine Learning, Data Analysis and Statistics using PYTHON,R and SAS 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. What you'll 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 Who this course is for: Data Scientist or Analyst You are interested in fraud detection for credit cards, insurance or health care, intrusion detection for cyber-security, or military surveillance for enemy activities and et cetera Course Info: Title: Outlier Detection Algorithms in Data Mining and Data Science Description Course: Outlier Detection in Data Mining, Data Science, Machine Learning, Data Analysis and Statistics using PYTHON,R and SAS Instructor: KDD Expert Duration: 2.5 hours on-demand video Online Classes Platform: Udemy GET Udemy Discount Outlier Detection Algorithms in Data Mining and Data Science 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 You can hone your programming skills because all algorithms you'll learn have implementation in PYTHON, R and SAS


Interesting Policy Reads: Maine's New Privacy Law, Possible SCOTUS Activity on Web Accessibility, and a Multinational AI Partnership

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Maine's New Privacy Law Means Well, but Goes Wrong, The Hill, August 13, 2019. A University's Online MBA Is Less Expensive--and Purposely Different, Inside Higher Ed, August 14, 2019. Wireless Carrier Throttling of Online Video Is Pervasive, Bloomberg, August 19, 2019. Maine's New Privacy Law Means Well, but Goes Wrong, The Hill, August 13, 2019. A University's Online MBA Is Less Expensive--and Purposely Different, Inside Higher Ed, August 14, 2019.


Tutorial on Outlier Detection in Python using the PyOD Library

#artificialintelligence

My latest data science project involved predicting the sales of each product in a particular store. There were several ways I could approach the problem. But no matter which model I used, my accuracy score would not improve. I figured out the problem after spending some time inspecting the data – outliers! This is a commonly overlooked mistake we tend to make.


100 Best Coursera Courses, Specializations, Classes 2020 JA Directives

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Are you looking for Best Coursera Courses 2020? You can earn a Coursera Certificate with Coursera free courses by applying for Coursera scholarship and by doing Coursera paid courses. You are going to get a 7-day free trial on Coursera when you join and start your very first subscription to do a Coursera Specializations for free. If you do not cancel your free trial you will be automatically transferred to paid subscription on the 8th Day. You can continue your Coursera Classes either by using Coursera App on mobile or on any other devices. This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. Learn and launch your career in Data Science with these best Coursera courses. A nine-course introduction to data science developed and taught by leading instructors. Develop programs to gather, clean, analyze, and visualize data. You will get new insights into your data. Learn to apply data science methods and techniques, and acquire analytical skills. This program is designed to take beginner learners to job readiness in about eight months.


Making \emph{ordinary least squares} linear classfiers more robust

arXiv.org Machine Learning

In the field of statistics and machine learning, the sums-of-squares, commonly referred to as \emph{ordinary least squares}, can be used as a convenient choice of cost function because of its many nice analytical properties, though not always the best choice. However, it has been long known that \emph{ordinary least squares} is not robust to outliers. Several attempts to resolve this problem led to the creation of alternative methods that, either did not fully resolved the \emph{outlier problem} or were computationally difficult. In this paper, we provide a very simple solution that can make \emph{ordinary least squares} less sensitive to outliers in data classification, by \emph{scaling the augmented input vector by its length}. We show some mathematical expositions of the \emph{outlier problem} using some approximations and geometrical techniques. We present numerical results to support the efficacy of our method.