Machine learning tools save you time by automating aspects of a machine learning project. There are platforms that you can use to work through a machine learning project end-to-end. There are also libraries that provide capabilities for one piece of a machine learning project. Using the right machine learning tools is as important as using the right machine learning algorithms. But […]
Getting started in machine learning can be frustrating. There’s so much to learn that it feels overwhelming. So much so that many developers interested in machine learning never get started. The idea of creating models on ad hoc datasets and entering a Kaggle competition sounds exciting a far off goal. So how did a Philosophy graduate get started in machine learning? […]
Programmers learn by implementing techniques from scratch. It is a type of learning that is perhaps slower than other types of learning, but fuller in that all of the micro decisions involved become intimate. The implementation is owned from head to tail. In this post we take a close look at Joel Grus popular book […]
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 […]
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 […]
In a 2010 post Hilary Mason and Chris Wiggins described the OSEMN process as a taxonomy of tasks that a data scientist should feel comfortable working on. The title of the post was “A Taxonomy of Data Science” on the now defunct dataists blog. This process has also been used as the structure of a […]
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 […]
