Machine learning algorithms are complex systems that require study to understand. Static descriptions of machine learning algorithms are a good starting point, but are insufficient to get a feeling for how the algorithm behaves. You need to see the algorithm in action. Experimenting on a running machine learning algorithms will allow you to build an […]
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 […]
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 R platform for statistical computing is perhaps the most popular and powerful platform for applied machine learning. The caret package in R has been called “R’s competitive advantage“. It makes the process of training, tuning and evaluating machine learning models in R consistent, easy and even fun. In this post you will discover the […]
The book Applied Predictive Modeling teaches practical machine learning theory with code examples in R. It is an excellent book and highly recommended to machine learning practitioners and users of R for machine learning. In this post you will discover the benefits of this book and how it can help you become a better machine […]
R is the most popular platform for applied machine learning. When you want to get serious with applied machine learning you will find your way into R. It is very powerful because so many machine learning algorithms are provided. A problem is that the algorithms are all provided by third parties, which makes their usage […]
Optimization is a big part of machine learning. It is the core of most popular methods, from least squares regression to artificial neural networks. In this post you will discover recipes for 5 optimization algorithms in R. These methods might be useful in the core of your own implementation of a machine learning algorithm. You […]
In this post you will discover 7 recipes for non-linear classification with decision trees in R. All recipes in this post use the iris flowers dataset provided with R in the datasets package. The dataset describes the measurements if iris flowers and requires classification of each observation to one of three flower species. Let’s get […]
Colorado Reed from Metacademy wrote a great post recently titled “Level-Up Your Machine Learning” to answer the question he often receives of: What should I do if I want to get ‘better’ at machine learning, but I don’t know what I want to learn? In this post you will discover a summary of Colorado recommendations […]
