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List of Machine Learning APIs

Below is a compilation of APIs that have benefited from Machine Learning in one way or another, we truly are living in the future so strap into your rocketship and prepare for blastoff.

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Practical Machine Learning in Python

Matt Spitz There are a plethora of options when it comes to deciding how to add a machine learning component to your python application. In this talk, I'll d...
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Brain decoding: Reading minds

Brain decoding: Reading minds | Semantic Intelligence | Scoop.it
By scanning blobs of brain activity, scientists may be able to decode people's thoughts, their dreams and even their intentions. (Patterns of brain activity + machine learning = mind reading?
Terry Woodward's insight:

Thought controlled devices are coming...

 

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Rescooped by Terry Woodward from Digital Humanities for beginners
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Twitter Data #Analytics. Data Mining and Machine Learning Lab | #Crawling

Twitter Data #Analytics. Data Mining and Machine Learning Lab | #Crawling | Semantic Intelligence | Scoop.it

Via luiy, Pierre Levy
Terry Woodward's insight:

Paper with code samples

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luiy's curator insight, August 31, 2013 2:11 PM

Social media has become a major platform for information sharing. Due to its openness in sharing data, Twitter is a prime example of social media in which researchers can verify their hypotheses, and practitioners can mine interesting patterns and build realworld applications. This book takes a reader through the process of harnessing Twitter data to find answers to intriguing questions. We begin with an introduction to the process of collecting data through Twitter's APIs and proceed to discuss strategies for curating large datasets. We then guide the reader through the process of visualizing Twitter data with realworld examples, present challenges and complexities of building visual analytic tools, and provide strategies to address these issues. We show by example how some powerful measures can be computed using various Twitter data sources. This book is designed to provide researchers, practitioners, project managers, and graduate students new to the field with an entry point to jump start their endeavors. It also serves as a convenient reference for readers seasoned in Twitter data analysis.

Rescooped by Terry Woodward from Street Photography
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Taking Street Photography to Another Level: Time-Squished Photos Turn Random Moments Into Patterns | Raw File | Wired.com

Taking Street Photography to Another Level: Time-Squished Photos Turn Random Moments Into Patterns | Raw File | Wired.com | Semantic Intelligence | Scoop.it
Pelle Cass has put a new spin on people-watching and taken street photography to another level with his project Selected People. For each photo in the series, he essentially crushes time-lapse photography into a single frame.

Via Street Shooters
Terry Woodward's insight:

Our digital ability to work with data over time is opening novel ways to look at patterns

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main-qimg-8638c2e478be3df1162cc1171ebea075 (1600x888 pixels)

main-qimg-8638c2e478be3df1162cc1171ebea075 (1600x888 pixels) | Semantic Intelligence | Scoop.it
@lboullu check this RT @brouberol Not sure about which machine learning algo fits your data? → http://t.co/QpDuoCU7sD #scikit-learn #python
Terry Woodward's insight:

Good chart for initial selection of ML approach

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Squat Rx: Producing Coherence in the Flow

Squat Rx: Producing Coherence in the Flow | Semantic Intelligence | Scoop.it
"You create a little turbulence," says Santa Fe Institute economist John Miller, who specializes in complex adaptive social systems. "By adding a little noise to the system you produce coherence in the flow." (Simplexity, pg.
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List of Machine Learning APIs

Below is a compilation of APIs that have benefited from Machine Learning in one way or another, we truly are living in the future so strap into your rocketship and prepare for blastoff.

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IBM Research: Buying local will beat online

IBM Research: Buying local will beat online | Semantic Intelligence | Scoop.it
Learn how cognitive systems will make local buying smarter than online.
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The Machine Learning Skills Pyramid

The Machine Learning Skills Pyramid | Semantic Intelligence | Scoop.it
Much has been written and discussed lately about the data scientist “unicorn” mythology where a single employee personifies a statistician, mathematician, computer scientist, DBA, coder, hardware guru, system admin, and C-suite liaison.
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Researcher Dreams Up Machines That Learn Without Humans | Wired Enterprise | Wired.com

Researcher Dreams Up Machines That Learn Without Humans | Wired Enterprise | Wired.com | Semantic Intelligence | Scoop.it
Yoshua Bengio recently had a vision -- a vision of how to build computers that can learn like people do.
Terry Woodward's insight:

Human brain seems to be good at predicting and filling in patterns for recognition almost to a fault - interesting to see experimental work starting to hint at how this intuitive process make work, and kudos to the researchers for open sourcing the ideas for experimentation!

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http://www.cmu.edu/news/stories/archives/2013/june/june19_identifyingemotions.html

Carnegie Mellon Researchers Identify Emotions Based on Brain Activity: Mind reading by applying machine learning http://t.co/dtyxKsxoJs
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mlpy - Machine Learning Python | pdg-technologi...

mlpy - Machine Learning Python | pdg-technologi... | Semantic Intelligence | Scoop.it
mlpy - Machine Learning Python on pdg-technologies.com curated by Kun Le (@kunlqt: mlpy is a Python module for Machine Learning built on top of NumPy/SciPy @scoopit http://t.co/ad8BB0bwR7)...
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Creatures of Coherence: Why We're So Obsessed With Causation -

Creatures of Coherence: Why We're So Obsessed With Causation - | Semantic Intelligence | Scoop.it
We default to cause-and-effect thinking to maintain control over our lives and everything that touches them, but some things just don't have clear answers.
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Rescooped by Terry Woodward from Papers
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The predictability of consumer visitation patterns

We consider hundreds of thousands of individual economic transactions to ask: how predictable are consumers in their merchant visitation patterns? Our results suggest that, in the long-run, much of our seemingly elective activity is actually highly predictable. Notwithstanding a wide range of individual preferences, shoppers share regularities in how they visit merchant locations over time. Yet while aggregate behavior is largely predictable, the interleaving of shopping events introduces important stochastic elements at short time scales. These short- and long-scale patterns suggest a theoretical upper bound on predictability, and describe the accuracy of a Markov model in predicting a person's next location. We incorporate population-level transition probabilities in the predictive models, and find that in many cases these improve accuracy. While our results point to the elusiveness of precise predictions about where a person will go next, they suggest the existence, at large time-scales, of regularities across the population.

 

The predictability of consumer visitation patterns

Coco Krumme, Alejandro Llorente, Manuel Cebrian, Alex ("Sandy") Pentland & Esteban Moro

Scientific Reports 3, Article number: 1645 http://dx.doi.org/10.1038/srep01645


Via Complexity Digest
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