Open Source vs Commercial Machine Learning Software
At the start of any machine learning project, you face an important choice: Which language or software should I use? Well, you have many options to choose from. Python, R,...
Explainers
Shorten the Learning Curve!
Download 10+ Bonus Resources for Data Science & Machine Learning
Overfitting in Machine Learning: What It Is and How to Prevent It
Did you know that there’s one mistake… …that thousands of data science beginners unknowingly commit? And that this mistake can single-handedly ruin your machine learning...
Best Practices for Feature Engineering
Feature engineering, the process creating new input features for machine learning, is one of the most effective ways to improve predictive models. Coming up with features is...
How to Handle Imbalanced Classes in Machine Learning
Imbalanced classes put “accuracy” out of business. This is a surprisingly common problem in machine learning (specifically in classification),...
WTF is the Bias-Variance Tradeoff? (Infographic)
Overheard after class: “doesn’t the Bias-Variance Tradeoff sound like the name of a treaty from a history documentary?” Ok, that’s fair… but...
Dimensionality Reduction Algorithms: Strengths and Weaknesses
Welcome to Part 2 of our tour through modern machine learning algorithms. In this part, we’ll cover methods for Dimensionality Reduction, further broken into Feature...
Modern Machine Learning Algorithms: Strengths and Weaknesses
In this guide, we’ll take a practical, concise tour through modern machine learning algorithms. While other such lists exist, they don’t really explain...
The 5 Levels of Machine Learning Iteration
Can you guess the answer to this riddle? If you’ve studied machine learning, you’ve seen this everywhere… If you’re a programmer, you’ve...
R vs. Python for Data Science: Summary of Modern Advances
Recently, some of our readers have been asking us about the best programming language for data science. Immediately, R and Python both come to mind… but which of these...