Overview of the most interesting materials on data analysis and machine learning No. 13 (September 8-14, 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. There are many interesting examples in this release using the R and Python programming languages. There are also some interesting articles about machine learning competitions. There are a lot of materials that will be interesting for beginners in the topic of data analysis and machine learning. Traditionally, a number of materials are devoted to the topic of Data Engineering.

Data Analysis and Machine Learning Materials

•   Experience gained in the Hunt for Prohibited Content competition on Kaggle
  An interesting post from the participant of the Hunt for Prohibited Content competition on Kaggle from AVITO.ru, which tells about the experience gained and ways to improve the results in machine learning competitions.
•    Implementation of the k nearest neighbors (kNN) method from scratch
  The author of the MachineLearningMastery blog provides an example of the implementation of the k nearest neighbors (kNN) method from scratch. This article uses the Python programming language.
•   Updating the list of online courses on Data Science
  This post provides a list of updates on online courses on Data Science.
•   New to machine learning? Avoid these three mistakes
  This article will be of interest primarily to beginners and will help to avoid three common mistakes when using machine learning.
•   Classification of time series: KNN and DTW
  The author gives an example of the classification of time series using K Nearest Neighbors & Dynamic Time Warping. Examples are implemented using the Python programming language.
•  Machine Learning Cheat Sheet I
  met a very interesting machine learning cheat sheet, which will help to quickly refresh my knowledge on the subject.
•   Visual evidence that neural networks can compute any function.
  I already mentioned the draft of Neural Networks and Deep Learning, in this case a chapter from a book that I found very curious called A visual proof that neural nets can compute any function. "
•  Benefits of Implementing Machine Learning Algorithms from the
  Ground Up The author of the MachineLearningMastery blog describes the benefits of implementing existing machine learning algorithms from scratch.
•    Kaggle's Bike Sharing Demand: Code Example
  I want to give you a small simple code example from a Kaggle machine learning competition called Bike Sharing Demand, in which participants are asked to predict the hourly quantitative need for bicycles at rental locations in Washington, DC
•   Clustering an image using the k-means method
  A small illustrative example of using clustering using the k-means method (k-means clustering) as applied to an image. The example uses the programming language R.
•   Visualization of the structure of a website using network graphs
  Sample code for visualizing the structure of a website using network graphs. This example uses the R programming language, as well as the RSiteCatalyst and d3Network libraries.
•   10 Libraries to Win Kaggle Competitions
  This slide set can help everyone improve their results in machine learning competitions on the Kaggle website.
•   Building a spam filter on R
  A fairly simple code example for building a spam filter using the R programming language, as well as using the Caret machine learning library and training using the support vector method (SVM).
•   From Hemp to Trees and Forests
  Another article from the Microsoft Technet Machine Learning Blog. This time, Chris Burges will talk about decision trees in a fairly simple language.
•  DataScienceCentral Weekly Digest
  Regular weekly data analysis digest from DataScienceCentral.
•   Introduction to Apache Kafka
  This Cloudera blog post is an introduction to Apache Kafka's distributed messaging system.
•    Online course “KIx: KIexploRx Explore Statistics with R”
  On edX, a course called “KIx: KIexploRx Explore Statistics with R” was first launched. The course will be primarily of interest to those who want to get acquainted with the R programming language and its practical application.
•   Is the data analytics profession right for me?
  A curious questionnaire article from the AnalyticsVidhya portal that will help you understand if the Data Scientist profession is right for you.
•   Effective indexing in MongoDB 2.6
  A small article that describes how to properly use indexing in the NoSQL MongoDB database, including the new indexing features introduced in version 2.6.
•    Video lectures from the Learning From Data course
  On September 25, a new session of the very popular online course will begin with edX's Learning From Data from California Institute of Technology and Professor Yaser Abu-Mostafa as the main instructor. But now a complete set of video lectures and practical exercises is available.
•   How data centers
  work Description of the data centers in the USA, presented in the form of visual infographics.
•  The best articles of KDnuggets (August 31 - September 6)
  List of the best articles of the portal of the popular KDnuggets from August 31 to September 6.
•  Top 10 Big Data Quotes Top 10 Big Data
  Quotes from the Smart Data Collective portal.
•    9 tips for choosing a NoSQL repository A
  series of articles that offers 9 tips for choosing a NoSQL repository.
•   High Performance Content Overview A
  weekly digest of the most interesting high performance content from the popular HighScalability portal.
•  180 top bloggers
  A list of 180 top bloggers on Data Science, proposed by DataScienceCentral.
•  Top Big Data sites
  A list of 6 Big Data resources that may be of interest to big data professionals, although most of you already know most of the resources.
•    Introduction to Big Data Architecture
  This article from the Cloudera blog is a good introduction to Big Data architecture and a description of what Big Data Engineer does.
•  5 levels of maturity Big Data in the company
  A small article with infographics about the different levels of maturity of the company in working with Big Data.
•    Review of Applied Predictive Modeling
  Review of a very curious machine learning book, Applied Predictive Modeling, by the author of the MachineLearningMastery blog.
•  Digest of the best resources from DataScienceCentral
  A good list of fresh interesting articles and resources from DataScienceCentral.
•    Example forecasting on R
  A small example of using the programming language R for forecasting with the machine learning competition Global Energy Forecasting Competition 2014.
•    Overview of the new Applied Spatial Analysis and Policy book Overview of the new Applied Spatial Analysis and Policy
  book on working with geospatial data in the programming language R.
•  News Data Mining
  A small list of interesting resources on the topic of Data Mining on September 10.
•  The best materials of the month
  A list of the best articles of the month on the topic of data analysis according to the version of the popular portal DataScienceCentral.
•    The third annual championship of the Russian AI Cup has begun
  As the Blog.Ru Group company’s blog on Habrahabr reported, the third annual championship of the Russian AI Cup called “CodeHockey” has begun. Last year, CodeTroopers reached the finals and was generally quite interesting, albeit very time-consuming. This year I also plan to try my hand at this competition.

Previous issue:  Overview of the most interesting materials on data analysis and machine learning No. 12 (September 1 - 8, 2014)