moat March 10, 2015 at 11:52

Overview of the most interesting materials on data analysis and machine learning No. 38 (March 2 - 8, 2015)

I present to you the next issue of a review of the most interesting materials on the topic of data analysis and machine learning.

General

How to speak the language "Data Science"
Bond. James Bond. Handwriting robotic fake for marketers and social engineers.
Seventh annual Microsoft Research Machine Learning and Intelligence Summer School - collaboration with ACM Europe
Google wants to measure the importance of sites by facts, not links
Wargaming and Yandex collaborate in Big Data
Who is going to make money on the upcoming boom of artificial intelligence systems?
EBay Launches Pulsar Open Source Tool for Taming Big Data
Licensing access to Big Data as a means of monetizing Twitter
Why Apache Spark is changing Silicon Valley
How to become an expert in the field of machine learning - some useful tips for beginners in the field of machine learning from the author of the MachineLearningMastery blog, which will help those who want to build a career in this field, but do not know where to start.
8 reasons for the popularity of Apache Spark
Three technologies that will change the Internet - a few words about now popular technologies based on the use of machine learning: speech recognition, image search and video analysis.
How PayPal uses Deep Learning to fight fraud
IBM acquires Deep Learning startup AlchemyAPI
Three key steps for building predictive machine learning applications
Spragunr library: Implementation of the Deep Q-learning algorithm based on Theano library

Search for texts that are not relevant to the topic and finding similar articles
Social Network Analysis: Spark GraphX
Creating a musical composition map is an interesting example of visual classification of musical compositions using machine learning and using the Python programming language.
Factor analysis
R: Plotting using the built-in visualization functionality - when mentioning the visualization topic using the programming language R, we often talk about the ggplot library, but do not forget about the built-in visualization capabilities of Base R Plots.
Machine learning traps. Measuring Model Efficiency (Part 1) - A discussion of the certainly important topic of measuring the performance of your predictive model.
Gradient model training using C #
Natural language processing using deep neural networks and the Torch library
Interactive visualizations using D3.js, DC.js, Python, and MongoDB
PageRank Calculation Example Using Apache Hadoop

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