Squark AI Tips Archive

Deep Learning is a category of machine learning with special advantages for some tasks and disadvantages for others. Machine learning workflows begin by identifying features within data sets. For structured information with relatively few columns and rows, this is straightforward.

Classifications are the most frequently used—and most useful—prediction types. Classifications are predictions that separate data into groups. Binary Classification produces “yes-no” or “in-out” answers when there are only two choices. Multi-Class Classification applies when there are three or more possibilities and shows probabilities

Predictions are interesting on their own. They are valuable when put into production. Operationalizing AutoML – often called “ML Ops” – means putting AutoML predictions into regular workflows to change business outcomes. Here are a few ways do it. Graphical

Productive uses for AI are closer at hand than ever due to the rise of AutoML. Beginning in 2019, advancements in AI have replaced obstacles with tools to benefit from its power. Foremost among them is Automated Machine Learning (AutoML), which does

Follow the money to see why marketing and sales are the most common applications for AI. Instant PaybackSmall improvements in marketing and sales can produce large returns quickly. Think of the impact of gaining a few percentage points on lead conversions,

Statistics and machine learning differ in method and purpose. Which is superior depends upon your goals. Statistics is a subset of mathematics that interprets relationships among variables in data sets. Statisticians make inferences and estimate values based solely on data collected

How Important Is Explainability? Understanding the inner workings of ML algorithms may distract from realizing benefits from good predictions. Explainability Explained With the rise of artificial intelligence has come skepticism. Mysteries of how AI works make questioning the “black box”

What Is A Confusion Matrix? The most aptly named AI term is actually simple. A Confusion Matrix is a table that shows how often an AI classifier gets confused predicting true and false conditions. Here is a simple example of

What Is Overfitting? Telltale Super-Accuracy on Training Data When machine learning models show exceptional accuracy on training data sets, but perform poorly on new, unseen data, they are guilty of overfitting. Overfitting happens when models “learn” from noise in data