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), […]
You do not need to know statistics before you can start learning and applying machine learning. You can start today. Nevertheless, knowing some statistics can be very helpful to understand the language used in machine learning. Knowing some statistics will eventually be required when you want to start making strong claims about your results. In […]
We all know the satisfaction of running an analysis and seeing the results come back the way we want them to: 80% accuracy; 85%; 90%? The temptation is strong just to turn to the Results section of the report we’re writing, and put the numbers in. But wait: as always, it’s not that straightforward. Succumbing […]
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 […]
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 […]
R is perhaps one of the most powerful and most popular platforms for statistical programming and applied machine learning. When you get serious about machine learning, you will find your way into R. In this post, you will discover what R is, where it came from and some of its most important features. Let’s get […]
R is a powerful platform for data analysis and machine learning. It is my main workhorse for things like competitions and consulting work. The reason is the large amounts of powerful algorithms available, all on the one platform. In this post I want to point out some resources you can use to get started in […]
David Mimno is an assistant professor in the Information Sciences department at Cornell University. He has a background and interest in Natural Language Processing (NLP), specifically topic modeling. Notably, he is the chief maintainer of MALLET, the Java-based NLP library. I recently came across a blog post by David titled “Advice for students of machine […]
If you are using the Python stack for studying and applying machine learning, then the library that you will want to use for data analysis and data manipulation is Pandas. This post gives you a quick introduction to the Pandas library and point you in the right direction for getting started. Let’s get started. Data […]
What is the best machine learning algorithm? I get this question a lot. Maybe even daily. Sometimes it’s a general question. I figure people want to make sure they are learning the one true machine learning algorithm and not wasting their time on anything less. Most other times it is with regard to a specific […]
