Do you have questions like: What data is best for my problem? What algorithm is best for my data? How do I best configure my algorithm? Why can’t a machine learning expert just give you a straight answer to your question? In this post, I want to help you see why no one can ever tell you what algorithm to use or how to configure it for your specific dataset. I want to help you see that finding good data/algorithm/configuration […]

Last Updated on September 4, 2020 Supervised machine learning is often described as the problem of approximating a target function that maps inputs to outputs. This description is characterized as searching through and evaluating candidate hypothesis from hypothesis spaces. The discussion of hypotheses in machine learning can be confusing for a beginner, especially when “hypothesis” has a distinct, but related meaning in statistics (e.g. statistical hypothesis testing) and more broadly in science (e.g. scientific hypothesis). In this post, you will […]

Last Updated on November 11, 2019 Machine learning is a large field of study that overlaps with and inherits ideas from many related fields such as artificial intelligence. The focus of the field is learning, that is, acquiring skills or knowledge from experience. Most commonly, this means synthesizing useful concepts from historical data. As such, there are many different types of learning that you may encounter as a practitioner in the field of machine learning: from whole fields of study […]