Written by Alexander Raginsky Bhavya Geethika Today thousands of scientists and engineers are applying machine learning to an extraordinarily broad range of domains. Equip yourself with machine learning skills in an all new way by reading this free ebook, by John Winn and Christopher Bishop (with Thomas Diethe). machine-learning-model-method “I am overwhelmed by the choice of machine learning methods
Written by Alexander Raginsky Your relationship with your customer has become increasingly personal. Your business is starving for customer analytics that help you better understand each individual customer — not just a broad customer segment. That focus on individual customers means we’re entering a world of extreme segmentation. This new focus is a key strategy for companies that realize
Written by Alexander Raginsky 2016 iConference Doctoral Colloquium Call for Submissions The application deadline is September 28, 2015, 11:59 pm EDT The Doctoral Colloquium will take place on Wednesday, March 23, 2016, the final day of the 2016 iConference. While the official Colloquium is a one-day event, other Colloquium-related events will be held throughout the iConference. Therefore, students are
Written by Alexander Raginsky Motivation On June 17, 2015, I celebrated my two year #Twitterversary @Twitter. Looking back, the Data Science (short for DS) landscape at Twitter has shifted quite a bit: Machine Learning has played an increasingly prominent role across many core Twitter products that were previously not ML driven (e.g. “While you are away”) Tool wise, we’ve
Written by Alexander Raginsky Type your job title into the search box below to find out the likelihood that it could be automated within the next two decades. Read more at http://www.bbc.com/news/technology-34066941 Source: BBC
Written by Alexander Raginsky I would like to share my learning experience on Machine Learning for the past few months : It was end of last year, I decided to research upon Machine learning (ML) and have been taking few little steps. I need to understand what it’s all about ML and related hype factor that it has created
Written by Alexander Raginsky From programmers who know statistics, to statisticians who know how to program; everyone that does data science work has their own unique set of skills and tools that they use to get their jobs done. We want to know the essential data science topics that you use in your work and in turn, share the
Written by Alexander Raginsky Introduction There has been a renewed interest in machine learning in last few years. This revival seems to be driven by strong fundamentals – loads of data being emitted by sensors across the globe, with cheap storage and lowest ever computational costs! However, not every one around understands what machine learning is. Here are a
Written by Alexander Raginsky From time to time, you still come across someone with the opinion that Big Data is nothing more than a fad, which will be forgotten about soon enough. You might not expect to hear this from me, but they’re actually right. Well – half right, at least! As I’ve written before, I’m not actually a
Written by Alexander Raginsky These predictions were published by the International Institute for Analytics (IIA). They produced a nice infographics, featured below, and re-tweeted many times by various bloggers, using the hash tag #2015Analytics. Other interesting predictions include those by Tableau, those by Pivotal, as well as my own predictions. Here are IIA’s predictions for 2015, in plain text:
