Overview of the most interesting materials on data analysis and machine learning №33 (January 26 - February 1, 2015)

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

• Why study R if you decide to do “data science”
• Data Science poster
• Deep Deep Learning Shortcomings is an interesting article from the KDnuggets blog about potential shortcomings of the popular Deep Learning machine learning method.
• Can Microsoft make R simple?
• A large list of resources on the topic of artificial intelligence - there are links to courses, books, programming resources, code samples, and a host of other interesting AI links.
• About machine learning in simple language - a brief article about the basics of machine learning from the authors of the blog Analytics Vidhya.
• New to Deep Learning? Here are 4 simple lessons from Google
• Big Data: 5 leading companies and their plans for 2015

Theory and algorithms of machine learning, code examples

• Linear regression on R - this publication describes 4 ways to use linear regression in the programming language R.
• When data is really a lot: Vowpal Wabbit
• Deep learning and Caffe on New Year's holidays
• Word2Vec in examples
• Providence Project: Using Machine Learning in Stack Exchange
• Creating a predictive model in Azure ML is a step-by-step example of creating a predictive model in the increasingly popular Microsoft Azure Machine Learning cloud product.
• Real-time tracking of the object from the drone using Python and OpenCV
• Eigenfaces: creating ghost images from a set of faces is quite an interesting article devoted to one of the theoretical aspects of face recognition.
• Deep Learning Statistical View (Part 1): Recursive Generalized Linear Models (Recursive GLMs)
• An example of the implementation of a committee of algorithms using scikit-learn is another good article from Sebastian Raschka, in this case it is an example of the implementation of a committee of algorithms, namely Weighted Majority Rule Ensemble Classifier using the library scikit-learn.
• An example of a predictive model in R: clustering by tag similarity is a small example of R code for data clustering from the author of the Caret library.
• spaCy: a modern library for natural language processing — an interesting library for NLP using Python.
• Parallel programming using GPU and R
• Introduction to the k-means flow method in Apache Spark 1.2
• A brief introduction to linear regression
• Using Apache Hadoop to predict flight delays (part 3) is the third part of a series of articles from the Hortonworks blog about the practical use of Apache Hadoop to predict flight delays.
• Deep learning for natural language processing: a selection of recent publications
• What is Supervised Learning and Unsupervised Learning?

Machine learning competitions

• Interviews with the winners of the American Epilepsy Society Seizure Prediction Challenge at Kaggle
• Trained perceptron in machine learning competitions

Online courses, educational materials and literature

• The beginning of the course “Linear algebra” on Coursera - February 2, the Coursera will begin a Russian-language course on linear algebra and presented by the National Research University Higher School of Economics.
• DataQuest: Online learning for machine learning is a recently launched free English-language platform for learning Data Science using Python.
• The online course “Linear Algebra - Foundations to Frontiers” started - on January 28, edX began a course on the basics of linear algebra from “The University of Texas at Austin: Linear Algebra - Foundations to Frontiers”.
• Coding the Matrix at Coursera - and another course dedicated to linear algebra, will begin in the coming days at Coursera: "Coding the Matrix: Linear Algebra through Computer Science Applications".
• Book review: Getting started with data science in the cloud
• Book: Beginning Data Science with R

Video, podcasts

• The introduction to Nando de Freitas Deep Learning is an interesting video from Dr. Nando de Freitas (Full-time Professor at Oxford). In this case, this is a lecture on the basics of Deep Learning.
• Talking Machines: Episode 3: Interview with Kevin Murphy - the third episode of "Talking Machines", in this case the interview with Kevin Murphy (Research Scientist, Google), in this episode touched upon such topics as using Torch, PyMC, Weka, Theano, Caffe and others.
• Introduction to Microsoft Azure Machine Learning - a video lecture on the basics of Microsoft Azure Machine Learning from the Microsoft Virtual Academy portal.

Data engineering

• Interesting numbers from the Apache Spark survey

Reviews

• Best material of the week from KDnuggets.com (January 18 - 24)
• Data Science News from MyDataMine.com (January 29)
• Big Data News from MyDataMine.com (January 26)
• Weekly Digest from DataScienceCentral (February 5)
• The best resources for the week from Data Elixir (№20)
• Weekly collection of the best materials from R1Soft (January 30)
• The most interesting materials on High Scalability (January 30)

Previous release: Review of the most interesting materials on data analysis and machine learning №32 (January 19 - 25, 2015)