Book Content
chapters • 5h36m total length
1. Overview of Probability Theory
2. Setting up the R Environment
3. Introducing Bayesian Inference
4. Machine Learning using Bayesian Inference
5. Getting to know Regression Models
6. Introducing Classification Models
7. Models for Unsupervised Learning
8. Probabilistic Graphical Models- Bayesian Networks
9. Big Data and Bayesian Inference
