Anomaly detection is to find data points that deviate from the norm. In other words, those are the points that do not follow expected patterns. Outliers and exceptions are terms used to describe unusual data. Anomaly detection is important in a variety of fields because it gives valuable and actionable insights. An abnormality in an […]
Machine learning involves the use of machine learning algorithms and models. For beginners, this is very confusing as often “machine learning algorithm” is used interchangeably with “machine learning model.” Are they the same thing or something different? As a developer, your intuition with “algorithms” like sort algorithms and search algorithms will help to clear up […]
What if I have more Columns than Rows in my dataset? Machine learning datasets are often structured or tabular data comprised of rows and columns. The columns that are fed as input to a model are called predictors or “p” and the rows are samples “n“. Most machine learning algorithms assume that there are many […]
In this post, we will take a tour of the most popular machine learning algorithms. It is useful to tour the main algorithms in the field to get a feeling of what methods are available. There are so many algorithms that it can feel overwhelming when algorithm names are thrown around and you are expected to […]
Data can change over time. This can result in poor and degrading predictive performance in predictive models that assume a static relationship between input and output variables. This problem of the changing underlying relationships in the data is called concept drift in the field of machine learning. In this post, you will discover the problem […]
You Don’t Have To Implement Algorithms …if you’re a beginner and just getting started. Stop. Are you implementing a machine learning algorithm at the moment? Why? Implementing algorithms from scratch is one of the biggest mistakes I see beginners make. In this post you will discover: The algorithm implementation trap that beginners fall into. The […]
Why Do You Get Different Results On Different Runs Of An Algorithm With The Same Data? Applied machine learning is a tapestry of breakthroughs and mindset shifts. Understanding the role of randomness in machine learning algorithms is one of those breakthroughs. Once you get it, you will see things differently. In a whole new light. Things like […]
Machine learning algorithms are a very large part of machine learning. You have to understand how they work to make any progress in the field. In this post you will discover a 14-part machine learning algorithms mini course that you can follow to finally understand machine learning algorithms. We are going to cover a lot […]
There are a lot of machine learning algorithms and each algorithm is an island of research. You have to choose the level of detail that you study machine learning algorithms. There is a sweet spot if you are a developer interested in applied predictive modeling. This post describes that sweet spot and gives you a […]
Boosting is an ensemble technique that attempts to create a strong classifier from a number of weak classifiers. In this post you will discover the AdaBoost Ensemble method for machine learning. After reading this post, you will know: What the boosting ensemble method is and generally how it works. How to learn to boost decision […]
