Dr. Daniel "Brian" Letort is a Fellow and Chief Data Scientist at Northrop Grumman Corporation. He has held various roles in his 18 year tenure, which have spanned software engineering, systems engineering, systems architecture, and chief architect. Throughout the roles, his interest have surrounded the strategic and forward-thinking use of data. Additionally, Brian serves as an adjunct instructor at both Colorado Tech and Southern New Hampshire University. Additionally, he serves as a lead faculty at Southern New Hampshire University.

However, some seasoned techniques are here to stay. At the top of the list are regression techniques. As long as this number is as high, you will encounter regressions during your machine learning career. Even if you don't use them yourself, it is essential to be aware of the different flavors and which problems they tackle. In this post, I provide you with a quick overview of five different (groups of) regressions.

So, you want to create a hugely successful machine-learning startup? Or you've been asked to start investigating ML for your firm? Well, you'd better get programming – but what language should you use? No languages have been designed specifically with ML in mind, but some do lend themselves to the task. Developers experimenting with machine learning will spend most of their time processing data sets, running them against a machine-learning algorithm, and then classifying them again until the results seem right.

Over the past few years, Machine Learning has taken a leading role in the discovery of data-driven solutions. Of these solutions, classification is by far one of the most commonly used areas of Machine Learning which is widely applied in fraud detection, image classification, ad click-through rate prediction, identification of medical conditions and a number of other areas. There is a range of different classification algorithms, but over the years single-model approach is being replaced by ensemble methods which combine a number of different algorithms and provide more accurate results than separate models. If you have ever tried to apply an ensemble method on a big data set you should have definitely run into a very common problem - the computation takes hours, sometimes even days or weeks, unless you have a powerful machine. At the Higgs Boson Data Science competition everyone's attention was caught by XGBoost - a new classification algorithm which outperformed all other Machine Learning algorithms used in this competition and brought the 1st place to its developers.