Summary: This is a discussion of social injustice, real or perceived, promulgated or perpetuated by machine learning models.  We propose a simple solution based on wide spread misunderstanding of what ML models can do.

This is a discussion of social injustice, real or perceived, promulgated or perpetuated by…

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, …

Who wants to hear a story about data?!

...(silence)...

Understandably, not a lot of people would raise their hands to an intro like that. For me, however, data has sculpted my career path and led to many exciting opportunities over the life of my profession. It hasn't been easy -- I think any entrepreneur would tell you the same and surely the content and media attention around self-starting individuals would concur. But I'm not here to tell you about the hardships of…

Blog post by Henry Hinnefield, Lead Data Scientist at Civis Analytics

"When machine learning models make decisions that affect people’s lives, how can you be sure those decisions are fair?"

In our work at Civis, we build a lot of models. Most…

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,…

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

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

Technical Resources

• Are the Digits of Pi Truly Random? - Updated, with Blockchain application and more
• Apache Spark with Scala - Learning Path…

Description

The disciplines of science and engineering rely heavily on the forecasting of prospective constraints for concepts that have not yet been proven to exist, especially in areas such as artificial intelligence. Obtaining quality solutions to the problems presented becomes increasingly difficult due to the number of steps required to sift through the possible solutions, and the ability to solve such problems relies on the recognition of…

Our edited book entitled as "Predictive Modeling and Optimization Methods in Science and Engineering ", IGI Global (Editors: Dookie Kim- South Korea, Sanjiban Sekhar Roy -India,Tim Länsivaara -Finland, Ravinesh Deo-Australia,Pijush Samui-India) will be available online very soon.Hope this book will help research scholar and machine learning enthusiasts working on predictive models.

To contribute a chapter, click …

This came in my mailbox today, sent from this source. I thought you would be interested in this.

Sample list of recent placement:

Abstract – Any one working within industries like the mobility, fintech, mobile money, payments, banking or InsureTech with little knowledge of data science is actually sitting on gold mine to explore and show what Data Science / AI can do for that company. For example by identifying new areas for innovative value creation through data science. Today every…

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, AI and…

This article was written by Carlos Mendoza.

Advancements in machine learning and artificial intelligence (AI) opens new doors for…

This article was written by Jason Brownlee.

Artificial neural networks are a fascinating area of study, although they can be intimidating when just getting started. There are a lot of specialized terminology used when describing the data structures and algorithms used in the field. …

This article was written by Thomas Maydon. Maydon is the Head of Credit Solutions at Principa and has primarily been involved in consulting, analytics, credit bureau and predictive modeling services.

Simplistically, analytics can be divided into four key categories. I'll explain these four in more detail…

This article was written by Hannah Augur.

Global warming. For a topic as massive, important, and (somehow) controversial, big data is a clear option for sorting through the muck. What information is reliable? What solutions are realistic? When a global…

AnalyticBridge is one of Data Science Central channels. Below is a selection of popular articles posted a while back:

• 16 Data Science Repositories 
• 12 Interesting Reads for Math Geeks …

Overview

Nowadays, there are numerous risks related to bank loans both for the banks and the borrowers getting the loans. The risk analysis about bank loans needs understanding about the risk and the risk level. Banks need to analyze their customers for loan eligibility so that they can specifically target those customers.

Banks wanted to automate the loan eligibility process (real time) based on customer details such as Gender, Marital Status, Age,…

This formula-free summary provides a short overview about how PCA (principal component analysis) works for dimension reduction, that is, to select k features (also called variables) among a larger set of n features, with k much smaller than n. This smaller set of k features built with PCA is the best subset of k features, in the sense that it minimizes the variance of the residual noise when fitting data to a…

Overview

In the customer management lifecycle, customer churn refers to a decision made by the customer about ending the business relationship. It is also referred as loss of clients or customers. Customer loyalty and customer churn always add up to 100%. If a firm has a 60% of loyalty rate, then their loss or churn rate of customers is 40%. As per 80/20 customer profitability rule, 20% of customers are generating 80% of revenue. So, it is very important…