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Machine Learning vs Statistics vs Statistical Learning in One Picture

Statistics, Statistical Learning, and Machine Learning are three different areas with a large amount of overlap. Despite that overlap, they are distinct fields in their own right. The following picture illustrates the difference between the three fields.

References…

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Added by Stephanie Glen on September 6, 2019 at 8:47am — No Comments

Regularization in Machine Learning

Regularization And Its Types

Hello Guys, This blog contains all you need to know about regularization. This blog is all about mathematical intuition behind regularization and its Implementation in python.This blog is intended specially for newbies who are finding regularization difficult to digest. For any machine learning enthusiast , understanding the mathematical…

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Added by Dhruv Gangwani on September 5, 2019 at 4:00pm — No Comments

Thursday News, September 5

Here is our selection of featured articles and technical resources posted since Monday:

Technical Resources

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Added by Vincent Granville on September 5, 2019 at 7:30am — No Comments

R Users’ Salaries from the 2019 Stackoverflow Survey

Interesting analysis done in R, about salaries of R developers broken down by country, featuring salary range and median salary. 

The dataset consists of survey answers from nearly 90,000 respondents. About 5,000 of them reported using R for “extensive development work over the past year”. The first filter used reduces the dataset from 88,883 respondents to 5,048. The second filter…

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Added by Capri Granville on September 5, 2019 at 7:00am — No Comments

Jobs, Unemployment and 45's Performance.

Despite the consuming controversy surrounding his presidency, POTUS 45 has been able to secure solid ratings on the performance of the economy over his so-far 30-month administration. And he certainly isn't bashful about taking credit for the successes, opining loudly and often that his tax cuts and de-regulation initiatives…

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Added by steve miller on September 4, 2019 at 8:39am — No Comments

NLP Picks Bestsellers – A Lesson in Using NLP for Hidden Feature Extraction

Summary:  99% of our application of NLP has to do with chatbots or translation.  This is a very interesting story about expanding the bounds of NLP and feature creation to predict bestselling novels.  The authors created over 20,000 NLP features, about 2,700 of which proved to be predictive with a 90% accuracy rate in predicting NYT bestsellers.

 …

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Added by William Vorhies on September 3, 2019 at 7:35am — No Comments

Common Errors in Machine Learning due to Poor Statistics Knowledge

Probably the worst error is thinking there is a correlation when that correlation is purely artificial. Take a data set with 100,000 variables, say with 10 observations. Compute all the (99,999 * 100,000) / 2 cross-correlations. You are almost guaranteed to find one above 0.999. This is best illustrated in may article How to Lie with P-values (also discussing how to handle…

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Added by Vincent Granville on September 2, 2019 at 3:00pm — No Comments

Wrestling with Data Science “Second Surgeries”? Try MVE!

Please join me in Las Vegas at Hitachi Vantara’s NEXT 2019 in October where I’ll be…

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Added by Bill Schmarzo on September 2, 2019 at 11:03am — No Comments

15 Articles and Tutorials about Outliers

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, correlation, ouliers, regression Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, time series, cross-validation, model fitting, and many more. To keep receiving these articles, …

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Added by Vincent Granville on September 1, 2019 at 10:00am — No Comments

Misuses of Statistics: Examples and Solutions

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, correlation, outliers, regression Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, time series, cross-validation, model fitting, dataviz, and many more. To keep receiving these articles, …

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Added by Vincent Granville on September 1, 2019 at 9:30am — No Comments

Weekly Digest, September 2

Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week. To subscribe, follow this link.  

Featured Resources and Technical…

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Added by Vincent Granville on September 1, 2019 at 6:30am — No Comments

Loop-Runtime Comparison R, RCPP, Python

The positive reactions on my last post: “Different kinds of loops in R” lead me to compare some different versions of loops in R, RCPP (C++ integration of R). To see a bigger picture, I apply the Python for-loop additionally. The comparison focuses on the runtime for non-costly tasks with different numbers of iterations. For comparison purpose I create vectors in the form of (R syntax):

Vector <- 1:k

k = (1.000, 100.000, 1.000.000)

 

The task is to…

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Added by Frank Raulf on September 1, 2019 at 4:30am — No Comments

Are Your Dashboards and Reports Ignored?

You’ve spent countless hours preparing a great executive dashboard for your organization. After a few months, you realize that no one is using it.

While you don’t receive any complaints about the dashboard, you also don’t receive any praise. It may feel like a literal gut punch when you went to great efforts to deliver what the organization said was needed. 

The First Sign of a Problem

You should be concerned when no one is complaining or…

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Added by Tricia Aanderud on September 1, 2019 at 3:00am — No Comments

Object Detection with 10 lines of code

This article was written by Moses Olafenwa.

One of the important fields of Artificial Intelligence is Computer Vision. Computer Vision is the science of computers and software systems that can recognize and understand images and scenes. Computer Vision is also composed of various aspects such…

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Added by Andrea Manero-Bastin on August 31, 2019 at 10:30am — 1 Comment

Can an ethical and algorithmically transparent cloud kitchen prevent future Amazon fires?

Can an ethical and algorithmically transparent cloud kitchen prevent future Amazon fires?

 

We often view AI with suspicion – but AI can be used to solve complex problems currently facing society where innovative approaches are needed

 

For many of us, the Amazon fires are disturbing and a…

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Added by ajit jaokar on August 31, 2019 at 5:14am — No Comments

You Had an Ongoing Data Breach for Months. How Could You Not Know?

When Anthem first announced the breach of 80 million of its records, FBI spokespersons credited Anthem with responding only weeks after the attack started.

Soon after, Brian Krebs, who blogs at KrebsOnSecurity, uncovered evidence that the attack might have started much earlier.

Why does it take so long for companies like Anthem to discover data breaches in progress and even longer to tell consumers?

Although the answer to that question remains complicated, one truth is…

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Added by Robert Dale on August 30, 2019 at 12:30am — No Comments

Thursday News, August 29

Here is our selection of featured articles, forum questions, and technical resources posted since Monday. Several great contributions were featured today. 

Resources

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Added by Vincent Granville on August 29, 2019 at 1:30pm — No Comments

Labeling with Active Learning

The Ugly Truth Behind All That Data

We are in the age of data. In recent years, many companies have already started collecting large amounts of data about their business. On the other hand, many companies are just starting now. If you are working in one of these companies, you might be wondering what can be done with all that data.

 

What about using the data to train a supervised machine learning (ML)…

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Added by Rosaria Silipo on August 29, 2019 at 11:30am — 2 Comments

Extreme Events Modeling Using Continued Fractions

Continued fractions are usually considered as a beautiful, curious mathematical topic, but with applications mostly theoretical and limited to math and number theory. Here we show how it can be used in applied business and economics contexts, leveraging the mathematical theory developed for continued fraction, to model and explain natural phenomena. 

The interest in this project started when analyzing sequences such as x(n) = { nq } = nq -…

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Added by Vincent Granville on August 29, 2019 at 7:30am — 4 Comments

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