Overview of the most interesting materials on data analysis and machine learning No. 6 (July 21 - 28, 2014)

In the next issue of the review of the most interesting materials devoted to the topic of data analysis and machine learning, a number of articles are devoted to the practical application of various types of regression. There is an interesting series of articles on the use of machine learning in trading. As usual, a lot of material is devoted to machine learning algorithms (including neural networks). There are some interesting video lectures, as well as many articles about the practical application of the R language in data analysis and machine learning.

Data Analysis and Machine Learning Materials

•  16 areas of analytics in comparison with Data Science
  An interesting comparison regarding the new discipline of Data Science with various areas of analytics (data mining, machine leraning, statistics, etc.)
•   Nonlinear regression in R
  4 types of nonlinear regression with examples in the programming language R.
•   Visualizing logistic regression using Shiny
  An article on creating interactive graphs to visualize logistic regression using Shiny for the programming language R.
•  Everything you wanted to know about machine learning, but were afraid to ask (Part 1)
  An interesting article on the basics of machine learning.
•  Everything you wanted to know about machine learning, but were afraid to ask (Part 2)
  Continuation of a series of articles on the basics of machine learning.
•   The difference between library () and require () in the R language
  A small article on when to use library () and require () in the R language. There is often confusion in this matter.
•  The use of machine learning for trading (Part 1)
  Introduction to the topic of the use of machine learning for trading.
•   Application of machine learning for trading (part 2)
  Continuation of the topic of using machine learning for trading.
•   Application of linear regression using R
  An article on the application of 4 types of linear regression using the programming language R.
•  Stanford University has published a large collection of datasets
  Stanford University has published a large collection of graph datasets (Stanford Large Network Dataset Collection), that is, data that are organized in the form of graphs or networks. It seems like a great dataset where you can experiment and hone your skills in data analysis and machine learning.
•  DataScienceCentral Weekly Digest
  Regular weekly data analysis digest from DataScienceCentral.
•   Introduction to convolutional neural networks (part 1)
  Introductory article about the now popular convolutional neural networks, written in a fairly simple language.
•   Introduction to convolutional neural networks (part 2)
  Continuation of the discussion about convolutional neural networks. In the second part, the author pays great attention to the theory of convolutional neural networks.
•  Datasets for machine learning
  A list of resources on which you can find a large number of interesting data sets for machine learning and data analysis.
•  The use of Markov chains in practice
  An example of the possible use of Markov chains in practical problems using banking lending and risk assessment as an example.
•   Penalized regression with R
  An article about applying Penalized Regression with the R programming language.
•   Search for duplicates using machine learning
  An interesting article written in a fairly simple language about the typical problem of finding duplicates in records using machine learning. This article provides sample code in Python.
•   Simplification of R code using the magrittr library and pipelines
  Simplification of code in the R programming language using the magrittr library, which allows you to apply the pipeline approach to the code.
•   MLlib - machine learning library for Apache Spark
  A short article about the MLlib machine learning library for the growing Apache Spark.
•    Deep Learning
  Quoc Le video lectures Quoc Le from the Google Brain team is introducing Deep Learning video lectures on Machine Learning Summer School (MLSS '14) in Pittsburgh.
•  10 types of linear regression
  A small article about the question of the correct choice of different types of linear regression in machine learning.
•   Using Machine Learning for Kaggle Competitions
  To improve your machine learning skills, it’s often important to study ready-made sample solutions from data analysts. This article discusses an example of solving a well-known problem from the Kaggle website - the death of Titanic passengers. The author of the solution uses the programming language R in his example.
•    Introduction to Machine Learning
  Link to the second edition of an excellent machine learning book.
•  The truth about startups in the field of Data Science
  Interesting discussions about the problems that you may encounter when creating a startup in the field of Data Science.
•   How to Improve Your Machine Learning Skills
  A good, short list of machine learning books to help you level up on this subject.
•   The experiment in Yandex. How to identify an attacker using machine learning
  An article about the use of machine learning in Yandex to attempt to classify an attacker and an ordinary user according to behavioral characteristics. The details of the operation of these algorithms, unfortunately, are not particularly disclosed.

Previous issue:  Overview of the most interesting materials on data analysis and machine learning No. 5 (July 7 - 21, 2014)