Check out part 2 of this excellent series of articles on becoming a data scientist, written by someone who spends their day recruiting data scientists. This installation focuses on learning.
Who were the most talked about athletes in the 2016 Rio Olympic Games? Which sport was most cited by users? What was the overall sentiment? This analysis by Expert System provides the detailed answers.
Has Deep Learning become synonymous with Artificial Intelligence? Read a discussion on the topic fuelled by the opinions of 7 participating experts, and gain some additional insight into the future of research and technology.
If researchers can’t understand a provided answer, it is not viable. They can’t write about techniques they don’t understand beyond “Here are the numbers. Look how pretty my model is.” Good research, that ain’t.
Check out this excellent (and exhaustive) article on becoming a data scientist, written by someone who spends their day recruiting data scientists. Do yourself a favor and read the whole way through. You won't regret it!
The dust has settled from ICML 2016, having been held in June in NYC. Read some perspective on what was offered at the conference and relevant takeaways from a reflective attendee.
Whenever there is a Big Data conversation, especially in sports, expectations have to be set correctly. Big Data isn’t perfect, but it is a lot better than the more superficial methods of making a judgment.
At the Machine Intelligence Summit in Berlin last week, Jeremy Wyatt, Professor of Robotics and Artificial Intelligence at University of Birmingham, was asked a few questions about his work in mobile robot task planning and manipulation.
Too often, we blame The Terminator for the public's misconceptions concerning machine learning. But do James Cameron and the Austrian Oak stand wrongfully accused?
Roland Memisevic, Assistant Professor at the University of Montreal and Chief Scientist at Twenty Billion Neurons, explores ideas on rethinking unsupervised learning, which he feels may explain what scientists have been doing wrong.
Which tool should I use for my data pipelines? Get some advice from a data scientist recently having gone through this pipeline tool selection process.
A model's ability to minimize bias and minimize variance are often thought of as 2 opposing ends of a spectrum. Being able to understand these two types of errors are critical to diagnosing model results.
Read a data-driven discussion on the plight of internally displaced persons (IDPs) in Nigeria, and see the real power of data science and data visualization.
This post discusses some considerations, options, and opportunities for automating aspects of data science and machine learning. It is the second place recipient (tied) in the recent KDnuggets blog contest.
This post provides a simplifying framework, an ontology for Machine Learning and some important developments in dynamical machine learning. From first hand Data Science product experience, the author suggests how best to execute Data Science projects.
How to Become a Data Scientist – Part 1
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