That’s not because companies are using the wrong methods or tools. Work-breakdown structure or analogy-based? Mechanical or judgmental combination? Function, use case, or story points?
In general, overfitting refers to the use of a data set that is too closely aligned to a specific training model, leading to challenges in
One of the biggest issues that India faces in water distribution and storage is that of water leakage AI also helps in detecting sources of
Step by step, AI has been permeating virtually every application we use. From consumer-facing interactions to more advanced predictive B2B analytics, AI and ML algorithms consume increasing
Data science interviews, like other technical interviews, require plenty of preparation. There are a number of subjects that need to be covered in order to
Pricing optimization is a powerful lever for revenue growth, yet it’s too often put in the too-hard basket by too many companies. This is because
Machine Learning Practitioners have different personalities. While some of them are “I am an expert in X and X can train on any type of
This post presents some common scenarios where a seemingly good machine learning model may still be wrong, along with a discussion of how how to
Machine learning practitioners have different personalities. While some of them are “I am an expert in X and X can train on any type of
Big data analytics is an amazing tool at the epicenter of the digital revolution. But it’s not foolproof. Here’s how successful companies deal with its
Summary:  Which is the most critical element in data exploration, statistics or data visualization? The answer is a little like the lyric ‘love and marriage,
Machine learning is often held out as a magical solution to hard problems that will absolve us mere humans from ever having to actually learn
