You want to learn machine learning or data science. You might want a job or the opportunity to get a job in machine learning or data science. Alternatively, you might be a student or in a data role and looking to accelerate your learning in the area. If you think your only options are to […]
Midwest.io is was a conference in Kansas City on July 14-15 2014. At the conference, Josh Wills gave a talk on what it takes to build production machine learning infrastructure in a talk titled “From the lab to the factory: Building a Production Machine Learning Infrastructure“. Josh Wills is a the Senior Director of Data […]
Which features should you use to create a predictive model? This is a difficult question that may require deep knowledge of the problem domain. It is possible to automatically select those features in your data that are most useful or most relevant for the problem you are working on. This is a process called feature […]
What does it take to do well in competitive machine learning? To really dig into this question, you need to dig into the people that do well. In 2010 I participated in a Kaggle competition to predict the outcome of chess games in the future. It was a fascinating problem because it required you to […]
Machine learning algorithms are complex. To get good at applying a given algorithm you need to study it from multiple perspectives: algorithmic, mathematical and empirical. It’s this last point I want to stress. You need to build up an intuition or how an algorithm behaves on real data. You need to work on lots of […]
If You Knew Which Algorithm or Algorithm Configuration To Use, You Would Not Need To Use Machine Learning There is no best machine learning algorithm or algorithm parameters. I want to cure you of this type of silver bullet mindset. I see these questions a lot, even daily: Which is the best machine learning algorithm? What […]
Feature engineering is an informal topic, but one that is absolutely known and agreed to be key to success in applied machine learning. In creating this guide I went wide and deep and synthesized all of the material I could. You will discover what feature engineering is, what problem it solves, why it matters, how […]
The Caret R package allows you to easily construct many different model types and tune their parameters. After creating and tuning many model types, you may want know and select the best model so that you can use it to make predictions, perhaps in an operational environment. In this post you discover how to compare […]
Selecting the right features in your data can mean the difference between mediocre performance with long training times and great performance with short training times. The caret R package provides tools to automatically report on the relevance and importance of attributes in your data and even select the most important features for you. In this […]
Machine learning algorithms are parameterized so that they can be best adapted for a given problem. A difficulty is that configuring an algorithm for a given problem can be a project in and of itself. Like selecting ‘the best’ algorithm for a problem you cannot know before hand which algorithm parameters will be best for […]
