Overview of the most interesting materials on data analysis and machine learning No. 23 (November 17 - 23, 2014)

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

Pandemics modeling using the Wolfram Language (Mathematica 10) using Ebola
Interesting from the world of R (November 10-16, 2014)
DataTalks 10/25/14: first meeting
IBM Launches Joint Master's Degrees in Big Data with Leading Russian Universities
Why Twitter is an easy target for social analytics
Google and Stanford Build a Neural Network Capable of Describing Photos
9 Skills to Become a Data Scientist
A little more material from Highload ++ 2014 is the last batch of slides from various speeches from the conference of developers of high-loaded systems HighLoad ++ 2014. Not all of them are related to machine learning and data analysis, but many may be interesting.
Apache Mahout vs. Weka is a small comparison of two popular products.

Introduction to Unsupervised Learning with scikit-learn
Efficiently clear text using Python
Introduction to Deep Learning in Python
Browse libraries for data analysis using Python
Factor analysis versus principal component analysis
Code example: dplyr - dynamic grouping by field
Code example: combining multiple data.frame in R
One-dimensional linear regression is a good article about one-dimensional linear regression.
Using exploratory data analysis to better understand the problem and improve the result is another interesting article from the author of the blog MachineLearningMastery. In this case, we will focus on the use of Exploratory Data Analysis.
Ask a Data Scientist: Learning without a teacher is another article from the popular insideBIGDATA portal from the Ask a Data Scientist series, in this issue we will focus on Unsupervised learning.
Forecasting visualization is a good article on the possibility of various forecasting visualizations using the programming language R.
Basics of data analysis using R - a good set of slides from a report on the basics of data analysis using the programming language R.

The book “Statistical Inference for Everyone” is a link to the free version of the book “Statistical Inference for Everyone” and links to additional materials that may be useful when working with the book.

Introduction to Revolution R Open and Deploy R Open
Video lectures from the Summer School of Programming (Machine Learning Summer School 2014, Reykjavik)
An introduction to the support vector machines method is a good lecture on the basics of the support vector machines method from one of the MIT courses.
An introduction to reinforcement learning is a good introduction to reinforcement learning.

Using full-text indexing and search in PostgreSQL
How and why Yandex disables its own data centers
Apache Hadoop - not only MapReduce - a short article from the Vidhya Analytics blog about the capabilities that Apache Hadoop has besides MapReduce.
Apache Hive on Apache Spark - an article from the Cloudera company blog - a demonstration of the work of Apache Hive on Apache Spark, which is becoming more and more obvious the MapReduce descendant when working with Apache Hadoop.
Big Data 101: Separation - the continuation of the discussion about the basics of distributed computing and data storage, in this case we will talk about separation (Partitioning).

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