Where does theory fit into a top-down approach to studying machine learning? In the traditional approach to teaching machine learning, theory comes first requiring an extensive background in mathematics to be able to understand it. In my approach to teaching machine learning, I start with teaching you how to work problems end-to-end and deliver results. […]
Where can you get good datasets to practice machine learning? Datasets that are real-world so that they are interesting and relevant, although small enough for you to review in Excel and work through on your desktop. In this post you will discover a database of high-quality, real-world, and well understood machine learning datasets that you […]
Has this happened to you? You are working on your dataset. You create a classification model and get 90% accuracy immediately. “Fantastic” you think. You dive a little deeper and discover that 90% of the data belongs to one class. Damn! This is an example of an imbalanced dataset and the frustrating results it can […]
How Do I Get Started In Machine Learning? I’m a developer. I have read a book or some posts on machine learning. I have watched some of the Coursera machine learning course. I still don’t know how to get started… Does this sound familiar? The most common question I’m asked by developers on my newsletter is: How do […]
The Ladder Approach That You Can Use To Become a Machine Learning Consultant Do you want to do machine learning and get paid for it? Be careful what you wish for. In this post I outline a blueprint that you can use to learn enough machine learning to help small businesses and start-ups with their […]
In this post I layout my manifesto for how you can get started and make progress in machine learning. In this post you will discover what machine learning is, why it matters, how to do it and how to identify and overcome your self-limiting beliefs. Why Get Started in Machine Learning I believe machine learning is an important […]
There was a recent question that asked “How to not waste-time/procrastinate while ml scripts are running?“. I think this is an important question. I think answers to this question show a level of organization or maturity in your approach to work. I left a small comment on this question, but in this post I elaborate […]
In a recent presentation, Ben Hamner described the common pitfalls in machine learning projects he and his colleagues have observed during competitions on Kaggle. The talk was titled “Machine Learning Gremlins” and was presented in February 2014 at Strata. In this post we take a look at the pitfalls from Ben’s talk, what they look like and how […]
You do not need to learn linear algebra before you get started in machine learning, but at some time you may wish to dive deeper. In fact, if there was one area of mathematics I would suggest improving before the others, it would be linear algebra. It will give you the tools to help you […]
In November 2014, Bernhard Scholkopf was awarded the Milner Award by the Royal Society for his contributions to machine learning. In accepting the award, he gave a layman’s presentation of his work on statistical and causal machine learning methods titled “Statistical and causal approaches to machine learning“. It’s an excellent one hour talk and I highly recommend that you watch […]
