It's not as popular as it once was, but Bayesian Analytics remains a powerful tool for more supervised learning exercises.
Despite all the hype around Deep Learning Models, and AI as a Service APIs, there's still a need for Data Scientists to explain - in simple terms - what factors influence a given prediction. And even more importantly, sometimes we want to construct a model that represents real world process, rather than have a input values feed into a programmatically optimized series of neural networks and produce a predicted value.
I am not attempting to argue Deep Learning is not effective. Deep Learning is the best tool we have right now for predicting outcomes. However, prediction alone does not necessarily lead to a better human understanding of what influences said prediction. In many cases a human can't understand why a Deep Learning model calculates its predictions because by the time we understand the current model, the Deep Learning routine has already updated based on new information. Case in point, Reinforcement Learning routines are always adapting to the decisions of Actors in near real time. We typically do not judge the efficiency of a Reinforcement Learning model based on the decisions it makes, but on the associated effects of the human actors interacting with the model - are players in the battle arena giving up when playing against bots? Are chatbot responses receiving poor reviews?
Bayesian Analytics or Bayesian Analysis uses a Bayesian Approach to create human understandable insights from models. "But Bayesian Statistics is so hard? It has lots of weird symbols and probabilities and stuff?" Yes, learning Bayesian Statistics can be a challenge, but the notation is no more complex than any other type of statistics, most of us were just taught frequentist stats first so it seems more intuitive.
Regardless, an easy way to learn Bayesian Analysis is using this book: https://www.amazon.com/Doing-Bayesian-Data-Analysis-Tutorial/dp/0124058884/ followed by this book: https://www.amazon.com/Bayesian-Analysis-Chapman-Statistical-Science-dp-...