Today I had the opportunity of running a #DataScience bootcamp in London. It was an all-day affair and although the attendees were engaged, I’m sure that by the end of the 6th hour they were… Read More »Data Science Bootcamp – Done
Exploring around the interwebs, I came across this article by Rupert Goodwins in ArsTechnica about debunking myths about Artificial Intelligence. HAL 9000 in the film 2001. It is a good read and it you have a… Read More »Artificial Intelligence – Debunking Myths
Now reading “The Master Algorithm” by Pedro Domingos. You can’t control what you don’t understand, and that’s why you need to understand machine learning—as a citizen, a professional, and a human being engaged in the… Read More »Now reading: The Master Algorithm
Last Thursday I attended a Cloudera Breakfast Briefing where Sean Owen was speaking about Spark and the examples were related to building decision trees and random forests. It was a good session in general. Sean started… Read More »Cloudera Breakfast Briefing and Tofu Scientists
Working with data and approaching data-based competitions
In a lot of machine learning and data science applications, it is not unusual to use matrices to represent data. It is indeed a very convenient way to keep the information but also to do… Read More »You have big data? MIT researchers can help shrinking it!
How to Research a Machine Learning Algorithm // A reblog from Machine Learning Mastery Algorithms are a big part of the field of machine learning. You need to understand what algorithms are out there, and… Read More »How to Research a Machine Learning Algorithm – Reblog
It’s not who has the best algorithm that wins. It’s who has the most data!
