Overview of the most interesting materials on data analysis and machine learning №10 (August 18 - 25, 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. In this release, a lot of interesting materials for beginners. There are a couple of interesting video. There are materials on Data Engineering. As usual, a number of articles are devoted to code examples related to data analysis and machine learning. And traditionally, several articles are devoted to the topic of participation in machine learning competitions.

Data analysis and machine learning materials

• MIT's Deep Learning Book
  The book from MIT in the very popular direction of machine learning Deep Learning. The book is not yet complete, but many chapters are already available to readers.
• Data processing with R
  A small book that can be useful to all who work with this data, using the R programming language, dedicated to processing and cleaning data in the preprocessing phase, which is known to take a lot of time and takes a lot of effort from data analysis specialists.
• The Hard Way to Learn Machine Learning - A Pony Story
  Nathan Taggart (Product Manager at New Relic) in this video tells his story of learning machine learning and about what mistakes to avoid in this challenging task. The video is designed for beginners in the topic of data analysis and machine learning.
• What is R
  A small capacious overview of the programming language R with a description of the advantages and disadvantages.
• What companies need to know about Big Data
  An article arguing that many companies might want to change the way they work with their data and focus more on modern trends in Data Science.
• Unstructured text data analysis guide
  The first part of a series of articles from the popular portal Analytics Vidhya, devoted to an interesting topic of text analysis. This article describes the basic problems and issues, in future articles will describe the details of the implementation of these issues.
• Analyzing data from Mario Garzia from Microsoft
  Data analyst Mario Garzia from Microsoft, in his article on the Microsoft Technet Machine Learning Blog, gives an interesting discussion about the current state of affairs in Data Science.
• 5 advantages of participating in machine learning competitions
  Another interesting article from the author of the blog MachineLearningMastery. This time we will discuss the benefits of participating in machine learning competitions at Kaggle.
• Time series visualization using googleVis library
  Not so long ago, the release of googleVis version 0.5.5 was released. This short post provides a very simple code example for visualizing time series using the googleVis library for the R programming language.
• Microsoft Azure DocumentDB
  A small article about Microsoft's new NoSQL database called Azure DocumentDB.
• The use of machine learning for trading (part 1)
  Introduction to the topic of using machine learning for trading. This series of articles has already been presented in reviews on data analysis and machine learning. In this case, the translation of the first part into Russian.
• Improving the performance of requests in Apache Hive using partitioning
  A small article from Cloudera’s blog about how you can improve the performance of Apache Hive requests by using partitioning.
• Announced a new online course on Coursera from Stanford University - Mining Massive Datasets
  September 29, 2014 at Coursera starts a very interesting online course from Stanford University - Mining Massive Datasets.
• Fast HDF5 with Pandas
  An example of working with the format of storing information HDF5 from the Pandas data analysis framework for the Python programming language.
• Interesting Deep Learning Resources
  The list of resources for the popular machine learning technology Deep Learning, compiled by the famous portal KDnuggets.
• This is not NoSQL vs. RDBMS, it's ACID + Foreign Keys vs. Eventual Consistency
  Some interesting discussions about NoSQL and RDBMS data warehouses.
• An example of solving the problem on Kaggle
  An example of a possible solution to the popular Kaggle machine learning competition is “Predict Bike Sharing Demand” using the Gradient Boosted Trees technique. The example uses the machine learning tool GraphLab Create.
• Logistic Regression Job Visualization
  In machine learning, logistic regression is often used. This short post presents the visualization of the work of logistic regression in the form of an animated image.
• Machine Learning and Computer Vision (Part 2) The second part of a series of articles from the Microsoft Technet Machine Learning Blog, dedicated to the use of machine learning in solving image recognition problems and the use of computer vision technologies. The article is small and written in simple language, without immersing in the details of this rather complex topic.
  
• Hadoop Ecosystem
  A small useful article that gives a brief description of the main elements of the Hadoop ecosystem.
• What is Big Data?
  An interesting little article in which the author discusses what Big Data is and an attempt is made to give the most simple description of this term.
• Using expression in R
  Interesting article about the use of the function expression () in the programming language R.
• Supervised learning block diagram of machine learning
  Many are familiar with this way of machine learning, like learning with a teacher (Supervised learning). In this short post in the form of a flowchart, a good visualization of a sequence of typical actions is presented during training with a teacher.
• 21 great schedule
  A few excellent examples of data visualization using various types of graphs and charts from the DataScienceCentral portal.
• How to successfully perform in competitions at Kaggle
  Another helpful article on how to successfully compete in machine learning competitions at Kaggle.
• Announcement of the Capstone project in the data analysis specialization from Coursera
  A small article announcing the Capstone project, which relates to the final phase of Data Science Specialization from Johns Hopkins University. You can join the project if all 9 specialization courses have been successfully completed.
• Sybil: Google's machine learning scaling system
  In this report, Tushar Chandra talks about the fate of Sybil on Google. Sybil is an important Google research project that implements various machine learning algorithms, allowing them to scale. This development is widely used by Google.
• Four main languages ​​for data analysis
  The results of the vote, conducted by the popular KDnuggets portal, about the most popular languages ​​used for data analysis.
• Math for machine learning
  The article is devoted to the question of the necessary mathematical skills necessary for the development of basic knowledge of machine learning. The author indicates that the article is a draft version and that over time additional information will appear in it.
• Where libraries are installed in RStudio
  A small article is devoted to the curious question of where RStudio installs libraries.
• 44 articles on data analysis
  An interesting selection of articles and resources from the best data analyst collected by DataScienceCentral

Previous release: Review of the most interesting materials on data analysis and machine learning №9 (August 11 - 18, 2014)