#ML Weekly 20150825 – The Best New Machine Learning Content of the Week

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Here is a roundup of Machine Learning content from this past week ordered by their social sharing volume.  I gather the most shared Machine Learning content in Twitter, Facebook, Google+ and LinkedIn.

“Featurizing Data: Spark and Beyond: Leverage data transformation capabilities in Spark with Machine Learning”
In this Hortonworks’ partner guest blog, Abhimanyu Aditya, Senior Product Manager and co-founder at Skytree, explains how Skytree APIs solve challenges facing data engineers, simplifies data preparation and data transformation, using Apache Spark on YARN with Hortonworks Data Platform (HDP).

Publish date: 8/19/2015
Social sharing volume in the past week:
74 42 497 0

“An Introduction to Distributed Machine Learning”
Building, managing, and even using distributed systems can be hard. For over 50 years, distributed systems experts have been working hard to achieve the vision of making many machines work harmoniously together as though they were one. With an increase in the volume of data being collected today, the need to efficiently distribute computation has greatly increased. Today, distributed computation is ubiquitous. For some problems, there are many existing implementations of distributed systems that can scale out computation efficiently, but there are many other problems where significant roadblocks prevent efficient distribution. In this blog post, I will provide perspective on the challenges and benefits of using distributed systems for modern day machine learning needs.

Publish date: 8/21/2015
Social sharing volume in the past week:
108 39 284 4

“5 Techniques To Understand Machine Learning Algorithms Without the Background in Mathematics – Machine Learning Mastery”
How To Learn Machine Learning without the Math

Publish date: 8/22/2015
Social sharing volume in the past week:
93 37 66 6

“4 Machine Learning Algorithms That Shape Your Life”
Software is getting smart. It’s a slow, uneven process — but it’s also seemingly unstoppable. One by one, the hard problems of machine learning are falling to powerful new theoretical tools, letting us build software that can do some truly impressive things.

Publish date: 8/20/2015
Social sharing volume in the past week:
195 25 75 22

“Machine Learning made simple with Ruby”
How is it possible to make automatic classification work properly without resorting to using external prediction services? Starting with Bayesian classification, you can use the ruby gem classifier-reborn to create a Latent Semantic Indexer. Hands on!

Publish date: 8/24/2015
Social sharing volume in the past week:
28 17 0 22

“8 Tactics to Combat Imbalanced Classes in Your Machine Learning Dataset – Machine Learning Mastery”
You are working on your dataset. You create a classification model and get 90% accuracy immediately. “Fantastic” you think. You dive a little deeper and discover that 90% of the data belongs to one class. Damn!

Publish date: 8/18/2015
Social sharing volume in the past week:
44 11 39 7

“Machine Learning to Grow the World’s Knowledge”
How does Quora use Machine Learning to grow the world’s knowledge? I talked about this in a presentation I gave at Stitchfix.

Publish date: 8/23/2015
Social sharing volume in the past week:
36 8 41 1

“‘Machine Teaching’ Research Promotes Personalized Learning”
Researchers at the University of Wisconsin at Madison are hoping that ‘machine teaching,’ a new system that harnesses the power of artificial intelligence, will be able to deliver ideal lesson for  each student based on their behavior and learning patterns. The concept of machine teaching is the reverse engineering of machine learning.

Publish date: 8/18/2015
Social
sharing volume in the past week:
17 9 8 2

“Hackathon project uses machine-learning to predict 50% of hotel cancellations”
Hotel cancellations are a big concern for the travel industry as rooms are held for people who may not even turn up at not just a significant cost to the business, but also preventing other travellers from using the room which may be required for their visit.

Publish date: 8/18/2015
Social sharing volume in the past week:
4 4 2 5

“Microsoft’s Azure Machine Learning opened to South East Asia”
A year and a half ago Microsoft began adding machine learning capabilities to its Azure cloud platform. The new platform was called Azure Machine Learning and was intended to bring together capabilities of robust algorithms and analytics tools that spanned Microsoft services. Since then, Microsoft’s worked and tweaked the service and in February of this year, offered Azure ML as a fully managed, fully supported cloud service.

Publish date: 8/20/2015
Social sharing volume in the past week:
22 7 2 1

“AWS launches Amazon Machine Learning into Europe – CloudHub”
Amazon Web Services (AWS) has launched Amazon Machine Learning into Europe, with its Dublin headquarters and data centre able to serve the predictive analytics service to the region.

Publish date: 8/24/2015
Social sharing volume in the past week:
5 1 23 0

“Why isn’t Apple investing in machine learning?”
People seem to be pushing back on this question, so I’m going to side with the OP here. From my pre-Google experience as a grad student at CMU and later as a Data Scientist at Quora I met a lot of people in and around the field of Machine Learning and no one was talking about Apple. They are not considered a big investor in Machine Learning. They don’t employ a large pool of ML researchers to my knowledge and I don’t see them at conferences or hear about their systems.

Publish date: 8/23/2015
Social sharing volume in the past week:
13 1 0 1

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