Probabilistic graphical models is a technique in machine learning that uses the concepts of graph theory to concisely represent and optimally predict values in our data problems.
Graphical models gives us techniques to find complex patterns in the data and are widely used in the field of speech recognition, information extraction, image segmentation, and modeling gene regulatory networks.
This book starts with the basics of probability theory and graph theory, then goes on to discuss various models and inference algorithms. All the different types of models are discussed along with code examples to create and modify them, and also run different inference algorithms on them. There is an entire chapter that goes on to cover Naive Bayes model and Hidden Markov models. These models have been thoroughly discussed using real-world examples.