Last Updated on March 5, 2020 We live in a world drowning in data. Internet tracking, stock market movement, genome sequencing technologies and their ilk all produce enormous amounts of data. Most of this data is someone else’s responsibility, generated by someone else, stored in someone else’s database, which is maintained and made available by… you guessed it… someone else. But. Whenever we carry out a machine learning project we are working with a small subset of the all the […]

Last Updated on August 15, 2020 You often jump from problem-to-problem in applied machine learning and you need to get up to speed on a new dataset, fast. A classical and under-utilised approach that you can use to quickly build a relationship with a new data problem is Exploratory Data Analysis. In this post you will discover Exploratory Data Analysis (EDA), the techniques and tactics that you can use and why you should be performing EDA on your next problem. […]

Last Updated on March 5, 2020 The most commonly reported measure of classifier performance is accuracy: the percent of correct classifications obtained. This metric has the advantage of being easy to understand and makes comparison of the performance of different classifiers trivial, but it ignores many of the factors which should be taken into account when honestly assessing the performance of a classifier. What Is Meant By Classifier Performance? Classifier performance is more than just a count of correct classifications. […]

Last Updated on September 5, 2016 In a recent presentation at MLConf, Xavier Amatriain described 10 lessons that he has learned about building machine learning systems as the Research/Engineering Manager at Netflix. In this you will discover these 10 lessons in a summary from his talk and slides. Lessons Learned from Building Machine Learning Systems Taken from Xavier’s presentation 10 Lessons Learned The 10 lessons that Xavier presents can be summarized as follows: More data vs./and Better Models You might […]