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

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

Modeling pandemics using the Wolfram Language (Mathematica 10) using the example of Ebola
Interesting from the world of R (November 10-16, 2014)
DataTalks 10.25.14: First meeting
IBM launches a joint master’s program in the field of 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 needed to become a Data Scientist
A few more materials from Highload ++ 2014 are the last batch of slides from various speeches from the HighLoad ++ 2014 Highload Systems Developer Conference. 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.

Unsupervised learning with scikit-learn
Efficient text cleaning using Python
Introduction to Deep Learning in Python
Overview of libraries for data analysis using Python
Factor analysis versus principal component method
Example code: dplyr - dynamic grouping by field
Example code: merge multiple data.frame to 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 discuss the use of exploratory data analysis (Exploratory Data Analysis).
Ask a Data Scientist: Teaching without a teacher is another article from the popular portal InsideBIGDATA from the series “Ask a Data Scientist”, this issue will cover Unsupervised learning.
Forecasting visualization is a good article dedicated to the possibility of forecasting (rendering) various visualizations using the R programming language.
Fundamentals of data analysis using R - a good set of slides from the 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 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 the topic of reinforcement learning (Reinforcement learning).

Using full-text indexing and searching in PostgreSQL
How and why does Yandex disable its own data centers
Apache Hadoop is not just MapReduce - a small article from Analytics blog Vidhya about the features that Apache Hadoop has in addition to MapReduce.
Apache Hive on Apache Spark - an article from Cloudera’s blog — a demonstration of Apache Hive’s work on Apache Spark, which is increasingly becoming the heir of MapReduce 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 it will be about partitioning.

Source: https://habr.com/ru/post/243967/