Review of the most interesting materials on data analysis and machine learning №24 (November 24 - 30, 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

• Interesting from the world of R (November 17-23, 2014)
• Analyze text with Azure Machine Learning
• We share experience: features of preparing Russian-language text documents for analysis in the R environment - the first article from the new rubric from the site “R: Analysis and Visualization of Data” titled “We Share Experience”, the idea of ​​which is to publish guest messages written by readers of the blog.
• And once again about the recognition of numbers
• Adaptive learning, or a few words about Knewton
• Netflix: 10 lessons learned in building machine learning systems - a good set of slides from the Xavier Amatriain presentation (Director Algorithms Engineering, Netflix) called “10 lessons learned from building ML systems” from the MLconf conference.
• How to form successful Data Science teams
• How to become Data Scientist in 4 steps is another set of tips on how to achieve success in Data Science, in this case Vincent Granville gives advice.
• Show good results to get a job in the field of machine learning - a great article from the author of the blog MachineLearningMastery, in which he will tell you that it is not necessary to have some kind of prestigious education in order to get a job in machine learning.
• The graph-tool library for Python is an interesting library for analyzing graphs tool-graph for the Python programming language.
• Vladimir Vapnik is now working on the Facebook team - one of the most famous people in the field of machine learning and one of the authors of the Support vector machines method joined the team working on artificial intelligence issues at Facebook.
• 4 interesting articles from Vincent Granville - a small list of 4 articles that the author of the Data Science Central portal recommends reading.
• The future of Big Data is a good infographic from the popular SmartData Collective portal.
• Andrew Ng on Deep Learning and Silicon Valley Innovations - an interesting interview with Andrew Ng.
• 5 Things Every Data Science Team Leader Should Know
• Most Popular Slideshare Presentations on Data Science
• 3 major mistakes of companies when working with Big Data and ways to avoid them
• Most Popular Slideshare Presentations on Big Data
• 6 Tips to Help You Get a Job in Big Data
• The main trends of Big Data in 2015
• 4 things about Big Data that startups need to know

Theory and algorithms of machine learning, code examples

• Recognize barcodes on images using Python and OpenCV
• Implementing a Distributed Deep Learning Network Using Apache Spark
• Python Matrix Factoring is a good article telling about the basics of matrix factorization with examples of code in the Python programming language.
• Interpreting linear regression coefficients in R
• Ask a Data Scientist: Data Leakage is another article from the popular portal insideBIGDATA from the series “Ask a Data Scientist”, this issue will deal with such an important concept in machine learning as Data Leakage.
• Predicting stock prices using machine learning and big data is a very interesting article with examples of the year about stock price prediction using machine learning and using Apache Spark.
• Sample code: Visualizing data distribution with Python — many examples of Python programming language code for visualizing data distribution.
• Sample Code: Logistic Regression in SAS and R
• Parameters for the Azure ML web service - an article from the Microsoft Technet Machine Learning blog about working with the Azure ML web service with a small sample code in the C # programming language.

Machine learning competitions

• New machine learning competitions - this post presents a small list of new machine learning competitions at Kaggle.

Online courses, educational materials and literature

• Announcement of the new online course “Convolutional Neural Networks for Visual Recognition” - a new very interesting course has appeared on the site of Stanford University on the topic of using Convolutional Neural Networks for recognizing visual images.
• Deep Learning Master Class - this post presents materials from the Deep Learning Master Class held on November 5-6 in Tel-Aviv University.
• Review of the book "Predictive Analytics with Microsoft Azure Machine Learning"
• Review of the book "R Object-oriented Programming"

Video

• Understanding is a great video from TED - Susan Eliger: How to deal with big data? Nontrivial question about working with large data arrays of their processing and subsequent interpretation.

Data engineering

• Gobblin: a new framework for working with Big Data from LinkedIn
• BigBench: Measurements of the performance of Big Data Systems - a new product from Intel and Cloudera for measuring the performance of analytical systems.
• An introduction to Spark Streaming is a good article about the current fairly popular topic of using streaming in Apache Spark.
• Big Data Issues: Data warehouse performance is a short article that discusses such a big data issue as data warehouse performance.
• 5 errors in building data architecture that should be avoided
• MongoDB, Cassandra and HBase - 3 NoSQL databases, which should be monitored
• Types of databases and their evolution

Reviews

• Weekly Digest from DataScienceCentral (December 1)
• The best materials for the week from KDnuggets.com (November 16 - 22)
• The best resources for the week from Data Elixir (№12)
• Freakonometrics # 188 Most Interesting Materials
• Freakonometrics # 187 Most Interesting Materials
• Weekly collection of the best materials from R1Soft (November 29)

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