Overview of the most interesting materials on data analysis and machine learning No. 28 (December 22 - 28, 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.

General

•   The five most important events in the world R in 2014
•  How Deep Learning can be used in various areas of life in the future
•  The list of machine learning resources is an excellent list of machine learning resources divided into several thematic categories (Deep Learning, Online Learning, Ensemble Methods, Kernel Machines, GPU Learning, NLP, etc.)
•  Spark Packages Announcement - Spark Packages Announcement: A collection of various open source libraries for Apache Spark.
•  Interactive three-dimensional visualization of various data sets in a browser using data-projector
•  Forecasts for 2015: What will happen to Big Data and Data Science? - Another forecast for the coming year from the popular portal KDnuggets.com.
•  7 Best 2014 Big Data Resources from SmartData Collective
•  8 Big Data Predictions for 2015 by SmartData Collective
•  4 Steps to Big Data Success in 2015
•  Top 10 Data Science 2014 articles from Analytics Vidhya Blog

Theory and algorithms of machine learning, code examples

•    Hacker Guide to Neural Networks. Chapter 2: Machine Learning. Binary classification
•    Hacker Guide to Neural Networks. Chapter 2: Machine Learning. Support Vector Network (SVM) training
•    Overview of graph compression algorithms
•   InterSystems iKnow. Download data from Vkontakte
•    Linear Algebra in Machine Learning - The author of the MachineLearningMastery blog talks about what aspects of linear algebra are good to know for a better understanding of machine learning algorithms.
•   Collection and analysis of baseball statistics using R
•   Decision trees: growing and clipping branches
•    Using the Principal Component Analysis method for working with images
•   Handwriting recognition with R (part 1)
•    Comparison of working with Dataframe in Python, R and Julia
•   An example of creating predictive models using the caret library - An example of using the popular caret library for the R programming language to create predictive models.
•    Scikit-learn extended cheat sheet
•   A machine learning kit is a great useful set of various links to machine learning materials (including in Russian).
•   Data visualization using GAE Python, D3.js and Google BigQuery (part 1)
•   Data visualization using GAE Python, D3.js and Google BigQuery (part 2)
•   Data visualization using GAE Python, D3.js and Google BigQuery (part 3)
•   Spark Usage Example (Part 3): Cleaning and Sorting Social Security Numbers
•   Code example: applying a function to each row in data.frame

Machine Learning Competitions

•    How to approach problem solving on Kaggle - a set of simple tips from the author of the blog Analytics Vidhya, which will help to become a successful participant in the machine learning competitions at Kaggle.
•  Interview with Yann LeCun: convolutional networks and CIFAR-10 - an interesting interview with Yann LeCunn (Director of AI Research, Professor at New York University) on the use of convolutional networks and discussion of the recently completed KIFg-10 machine learning competition at Kaggle, which was dedicated to image recognition issues.
•   Kaggle Data Analysis with R - An interesting analysis of Kaggle machine learning competition data from the R programming language.

Online courses, training materials and literature

•   Announcement of the new online course "Statistics and R for the Life Sciences" - an interesting course called "Statistics and R for the Life Sciences" from Harvard University was announced on edX.
•   Convex Optimization Course Materials by Carnegie Mellon University
•   A set of lectures from the Principles of Distributed Computing (ETH Zürich) course

Videos

•    The main pitfalls of machine learning projects are an interesting presentation by Machine Learning Gremlins Ben Hammer and comments from the author of the MachineLearningMastery blog.
•   Machine Learning with the help of statistical methods and casual - a reference to the video presentation «Statistical and causal approaches to machine learningby Bernhard Scholkopf comments and blog author MachineLearningMastery him.

Data engineering

•   Azure Data Factory Preview

Reviews

•  Digest of the best resources from DataScienceCentral (December 29)
•  Best Content of the Week from KDnuggets.com (December 14 - 20)
•  The weekly collection of the best materials from R1Soft (December 26)
•  Best Resources of the Week from Data Elixir (No.15)
•  The most interesting materials from Freakonometrics No. 195
•  The most interesting materials from Freakonometrics No. 196
•  The most interesting materials from Freakonometrics No. 197

Previous issue:  Overview of the most interesting materials on data analysis and machine learning No. 27 (December 15 - 21, 2014)