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

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

General

How to speak the language of "Data Science"
Bond James Bond. Robotic fake handwriting for marketers and social engineers
The Seventh Annual Microsoft Research Summer School in Machine Learning and Intelligence - collaboration with ACM Europe
Google wants to measure the importance of sites on the facts, not links
Wargaming and Yandex collaborate in the field of Big Data
Who is going to make money on the coming boom of artificial intelligence systems?
EBay released the open source tool Pulsar for taming big data
Big Data Licensing as a Twitter Monetization Tool
Why Apache Spark is changing the Silicon Valley
How to become an expert in machine learning - some useful tips for machine learning beginners from the author of the blog MachineLearningMastery, who 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 technology that is now 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 machine-based predictive applications
The spragunr library: Implementation of the Deep Q-learning algorithm based on the Theano library

Search for texts that do not match the topic and find similar articles
Social Network Analysis: Spark GraphX
Creating a music map is an interesting example of visual classification of music using machine learning and the use of the Python programming language.
Factor analysis
R: Plotting with built-in visualization functionality - when mentioning the topic of visualization using the R programming language, people often talk about the ggplot library, but don't forget about the built-in visualization capabilities of Base R Plots.
Machine learning traps. Measuring the effectiveness of a model (Part 1) is a discussion of an absolutely important topic of measuring the performance of your predictive model.
Training the model using the gradient descent method using C #
Natural language processing using deep neural networks and the Torch library
Interactive Visualizations with D3.js, DC.js, Python, and MongoDB
Example of calculating PageRank using Apache Hadoop

Source: https://habr.com/ru/post/252597/