Once you have found and tuned a viable model of your problem it is time to make use of that model. You may need to revisit your why and remind yourself what form you need a solution for the problem you are solving. The problem is not addressed until you do something with the results. […]
Bojan Miletic asked a question about outlier detection in datasets when working with machine learning algorithms. This post is in answer to his question. If you have a question about machine learning, sign-up to the newsletter and reply to an email or use the contact form and ask, I will answer your question and may […]
Having one or two algorithms that perform reasonably well on a problem is a good start, but sometimes you may be incentivised to get the best result you can given the time and resources you have available. In this post, you will review methods you can use to squeeze out extra performance and improve the […]
Once you have defined your problem and prepared your data you need to apply machine learning algorithms to the data in order to solve your problem. You can spend a lot of time choosing, running and tuning algorithms. You want to make sure you are using your time effectively to get closer to your goal. […]
Machine learning algorithms learn from data. It is critical that you feed them the right data for the problem you want to solve. Even if you have good data, you need to make sure that it is in a useful scale, format and even that meaningful features are included. In this post you will learn […]
The first step in any project is defining your problem. You can use the most powerful and shiniest algorithms available, but the results will be meaningless if you are solving the wrong problem. In this post you will learn the process for thinking deeply about your problem before you get started. This is unarguably the […]
There are key concepts in machine learning that lay the foundation for understanding the field. In this post, you will learn the nomenclature (standard terms) that is used when describing data and datasets. You will also learn the concepts and terms used to describe learning and modeling from data that will provide a valuable intuition […]
Machine Learning is a multidisciplinary field and it can be very confusing when you are getting started to differentiate machine learning from the closely related fields of Artificial Intelligence and Data Mining. In this post you will learn about those fields that are related to machine learning. Specifically, you will learn about the boundaries of […]
Programmers should get involved in the field of machine learning because they are uniquely skilled to make huge contributions. In this post you will learn that as a programmer it can be easy to overlook the skills you have and overvalue those things you don’t know. You will learn about four opportunities for programmers to […]
It is important to know what is special right now to make machine learning an attractive field to study. Knowing why it is popular now can act like a guide together with knowledge of the promise that the field makes. It can highlight open questions and methods which are growth areas and why that may […]
