The use of large-scale data mining and machine learning has proliferated through the adoption of technologies such as Hadoop, with its simple programming semantics and rich and active ecosystem. This paper presents LinkedIn's Hadoop-based analytics stack, which allows data scientists and machine learning researchers to extract insights and build product features from massive amounts of data. In particular, we present our solutions to the ``last mile'' issues in providing a rich developer ecosystem. This includes easy ingress from and egress to online systems, and managing workflows as production processes. A key characteristic of our solution is that these distributed system concerns are completely abstracted away from researchers. For example, deploying data back into the online system is simply a 1-line Pig command that a data scientist can add to the end of their script. We also present case studies on how this ecosystem is used to solve problems ranging from recommendations to news feed updates to email digesting to descriptive analytical dashboards for our members.
Predictive analytics includes a diversity of methods, from statistics and modelling to machine learning and data mining, that analyse existing and historical data to make predictions about upcoming, or otherwise unknown, events.
Prediction is at the heart of almost every scientific discipline, and the study of generalization (that is, prediction) from data is the central topic of machine learning and statistics, and more generally, data mining.
Data mining, an interdisciplinary subfield of computer science, involving the methods at the intersection of artificial intelligence, machine learning and database systems. The Journal of Data Mining & Digital Humanities concerned with the intersection of computing and the disciplines of the humanities, with tools provided by computing such as data visualisation, information retrieval, statistics, text mining by publishing scholarly work beyond the traditional humanities. ...
Start with a big bunch of random branching decisions and then keep pruning (let evolutionary algortihms do the job of fitting) until you reach servicable predictions - that is the RF in action. This article briefly tells how this works.
Conference theme: Intersection of learning analytics research, theory and practice The International Learning Analytics and Knowledge conference is now in its fourth year! LAK 14 will keep up the momentum generated in the ...
Meet the startups making machine learning an elementary affair GigaOM The choices are getting a lot better for businesses that want out-of-the-box functionality for machine learning, predictive analytics and general data science.
Last week at Berlin Buzzwords 2013, MapR’s Ted Dunning showed how to do this with both metrics and with many forms of machine learning in his fourth #bbuzz talk titled “Real-time Learning for Fun and Profit,” presented to a packed room.
Great piece about the link from "now" to "various windows of "then" in data stream mining.
For a recently taken course in Machine Learning, a substantial part involved learning and applying linear classifiers and clustering algorithms on smaller data sets. In order to summarise the most important material, I created a cheat sheet in LaTeX.
Awesome gift for a friend who is deep into algorithms.
Special Issue of BJET Teacher-led inquiry, learning design and learning analytics: a virtuous circle. Guest editors: Dr Yishay Mor, Dr Rebecca Ferguson and Professor Barbara Wasson. Deadline for submissions: 2 September ...
Cover of learning analytics report Just published, Learning Analytics for Open and Distance Education, an edition of CEMCA EdTech Notes. This is a topical start-up guide series on emerging topics in the field of educational ...
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