Just a few years ago, most of things we consider machine learning (ML) technologies nowadays were a part of statistics; neural networks with more than three layers were considered deep. Word gradient was firmly associated with colour pallet, not with optimisation algorithm. I start every morning with reading a bunch of latest news about artificial intelligence (AI). And sometimes it seems that AI can do everything. Has Skynet already won, and I just did not receive a memo?

Welcome to the second chapter of ML lessons from Ng's experience. This will be a short one and we will only be talking about Human Level Performance & Avoidable Bias. If you did not read the first chapter, here's the link. Oh and one more thing, this series is entirely based on a recent course by Andrew Ng on Coursera. In the last few years, a lot more machine learning teams have been talking about comparing the machine learning systems to human level performance.