Bayesian Learning

The abstract uncertainty of Bayesian learning is a problem of artificial intelligence. Bayesian learning outlines a mathematically reliable way to handle uncertainty based on Bayes' theorem. The theory establishes a way to calculate the probability of occurrence of future events and gives some posterior probabilities for predicting events based on evidence and evidence of past events. Its application in artificial intelligence has been successful in many research fields and applications, including the development of cognitive models and neural networks.

Basis of CrowdSmart method is Bayesian learning. Let's say you have heard of new startups in the field of knowledge. Friends will ask you about the likelihood of your success, or the return on investment. If you know this field, you may get a successful estimate (probability) based on the story. Bayesian learning is a rigorous and rigorous way to correct beliefs and knowledge based on new data. In the case of example, you ask several questions, obtain data and evidence, then update the estimate. This is Bayesian learning: Always evaluate evidence and update beliefs. When data is collected from evaluators, investors, and customers, the system collects and organizes data and creates the latest estimates.

The first focal region in applying Bayesian reasoning is Bayesian linear modeling. In this article I am trying to introduce the concept of Bayesian linear regression. We will review the frequency linear regression method easily, introduce the Bayesian interpretation, and look at some of the results applied to a simple data set. I left the code of this article, but it is in the GitHub of the Jupyter notebook.

Even after struggling for weeks on Bayesian linear model theory and blog posts, I can not say that I completely understand the concept. Therefore, the way of thinking through practice is the most effective technology. I started using Bayesian linear regression as my selected machine learning model for data science project. This article is the first of two articles that documented the project. I'd like to show you an example of a complete data science pipeline, so the first article will focus on problem definition, exploratory data analysis, and benchmark setup. In Part 2, we will focus on completely achieving Bayesian linear regression and interpreting the results. So, if you already have EDA please go on. Otherwise, or just want to see a good plot, stay here. I will show you how to start with the problem of data science.