Bayesian Statistics
Bayesian statistics is an enthralling field that is now at the heart of many statistical applications in data science and machine learning. The Bayes Theorem, Bayesian networks, Enumeration & Elimination for inference in such networks, sampling methods such as Gibbs sampling and the Metropolis-Hastings algorithm, Bayesian inference, and its relationship to machine learning will all be covered in this course.
This course, one of the best online Bayesian courses, is built around examples and exercises that allow you to develop intuition and put what you've learned into practice. Many of the examples are drawn from real-world applications in science, business, and engineering, as well as data science job interviews.
Despite the fact that this is not a programming course, it contains numerous references to programming resources related to Bayesian statistics. The course is designed specifically for students who have not had many years of formal mathematics education. The only prerequisites are a basic understanding of probability and high-school level mathematics, ideally a first-year university mathematics course.
Who this course is for:
- University students in science, business and engineering interested in learning about Bayesian Statistics for university or job interviews
- Practitioners in these fields interested in learning the central concepts of Bayesian statistics to apply them to real-world problems
Requirements
- High school level mathematics / ideally first-year university mathematics or statistics course
- Basic background in probability
Course ratings: 4.4/5
Enroll here: https://www.udemy.com/course/bayesian-statistics/
