Episode 2 AI and Machine Learning Terminology

machinelrn

Welcome to the MachineLrn Podcast. We are a leading online resource for hands-on implementation of AI, deep learning, and machine learning. There are lots of theory-oriented web resources for this exploding field, but there is very little information on the actual engineering of ML solutions into real world enterprises and networks. This is a short episode and is “technically” episode #2 in the MachineLrn Podcast series. The first episode set the proper mood with the first rap song (in history) written about AI. It was entitled “AI State of Mind” and you can listen to it here: https://soundcloud.com/machinelrn/ai-state- of-mind Today we will keep it short. Powerful as it is, ML is chockablock full of technical jargon, algorithms and totally non-intuitive terminology. Collectively, this jumble of confusing terms and abbreviations create a tremendous barrier for newbies. Cynics may argue this was done on purpose, a way to hide simple concepts from newcomers and customers alike ? But the reality is that ML is a field with very deep historical roots across the math, computer science, and data research fields. The result is an enormous corpus of historical AI terminology which is at once beautiful, awesome, (and for most) incomprehensible to behold. Regardless of your role (manager, engineer, student, CEO) you should become familiar with the terminology of AI, the concepts and the most frequently used terms and abbreivations. To kick this journey off on the right foot, here are “Lloyd’s dirty dozen” - basic ML terms everyone should understand, regardless of background or position: