Tag: unsupervised learning

DataCamp – Unsupervised Learning in Python

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From DataCamp: “Say you have a collection of customers with a variety of characteristics such as age, location, and financial history, and you wish to discover patterns and sort them into clusters. Or perhaps you have a set of texts, such as wikipedia pages, and you wish to segment them into categories based on their content. This is the world of unsupervised learning, called as such because you are not guiding, … Continue Reading

DataCamp – Unsupervised Learning in R

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From DataCamp: “Many times in machine learning, the goal is to find patterns in data without trying to make predictions. This is called unsupervised learning. One common use case of unsupervised learning is grouping consumers based on demographics and purchasing history to deploy targeted marketing campaigns. Another example is wanting to describe the unmeasured factors that most influence crime differences between cities. This course provides a basic introduction to clustering and … Continue Reading

Data Intelligence Conference

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Use Promo Code FRIENDSANDFAMILY30  for 30% off your registration fee! Next Event: June 23-25, 2017 – McLean, Va     Friday June 23: 12pm-6pm     Saturday June 24: 10am-6pm     Sunday June 25: 10am-3pm See full schedule From Data Intelligence: “This event aims to bring machine learning engineers and researchers together to share ideas and projects amongst both academic and professional practitioners in the field.” 2017 Speakers

Predictive Analytics World – Chicago

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2017 Event: June 19-22, 2017 – Chicago, IL Co-Located with PAW Manufacturing Conference: http://www.predictiveanalyticsworld.com/mfg/2017/ From Predictive Analytics World: Predictive Analytics World is the leading cross-vendor event for predictive analytics professionals, managers and commercial practitioners. The only conference of its kind, Predictive Analytics World delivers vendor-neutral sessions across verticals such as banking, financial services, e-commerce, entertainment, government, healthcare, manufacturing, high technology, insurance, non-profits, publishing, and retail. 2017 Speakers

Predictive Analytics World – San Francisco

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2017 Event: May 14-18, 2017 From Predictive Analytics World: Predictive Analytics World is the leading cross-vendor event for predictive analytics professionals, managers and commercial practitioners. he only conference of its kind, Predictive Analytics World delivers vendor-neutral sessions across verticals such as banking, financial services, e-commerce, entertainment, government, healthcare, manufacturing, high technology, insurance, non-profits, publishing, and retail. Overview of PAW Conference 2017 Speakers  

NIPS (Neural Information Processing Systems) Conference

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From NIPS Conference site: “The Thirty-first Annual Conference on Neural Information Processing Systems (NIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting” Next event:  December 4 – 9, 2017 – Long Beach, CA Registration begins August 17, 2017 Videos from NIPS Conference 2016: NIPS … Continue Reading

The Elements of Statistical Learning

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This book describes the important ideas in statistics, data mining, machine learning, and bioinformatics in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics.

Practical Data Science with R

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Practical Data Science with R shows you how to apply the R programming language and useful statistical techniques to everyday business situations using examples from marketing, business intelligence, and decision support.

Metis Data Science Bootcamp

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Learn Data Science in 12 weeks with 100% in-person instruction from expert data scientists.

Predictive Analytics 3 – Dimension Reduction, Clustering and Association Rules

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In this online course, you will cover key unsupervised learning techniques: association rules, principal components analysis, and clustering.