Can I read Hands-On Markov Models with Python : Implement Probabilistic Models for Learning Complex Data Sequences Using the Python Ecosystem on EtoBox?

Hands-On Markov Models with Python : Implement Probabilistic Models for Learning Complex Data Sequences Using the Python Ecosystem by Ankur Ankan & Abinash Panda [Ankan, Ankur & Panda, Abinash] is a nonfiction available to read on EtoBox.

What is Hands-On Markov Models with Python : Implement Probabilistic Models for Learning Complex Data Sequences Using the Python Ecosystem about?

Unleash the power of unsupervised machine learning in Hidden Markov Models using TensorFlow, pgmpy, and hmmlearnKey FeaturesBuild a variety of Hidden Markov Models (HMM)Create and apply models to any sequence of data to analyze, predict, and extract valuable insightsUse natural language processing (NLP) techniques and 2D-HMM model for image segmentationBook DescriptionHidden Markov Model (HMM) is a statistical model based on the Markov chain concept. Hands-On Markov Models with Python helps you get to grips with HMMs and different inference algorithms by working on real-world problems. The hands-on examples explored in the book help you simplify the process flow in machine learning by using Markov model concepts, thereby making it accessible to everyone.Once you've covered the basic concepts of Markov chains, you'll get insights into Markov processes, models, and types with the help of practical examples. After grasping these fundamentals, you'll move on to learning about the different algorithms used in inferences and applying them in state and parameter inference. In addition to this, you'll explore the Bayesian approach of inference and learn how to apply it in HMMs.In further c

Who reads Hands-On Markov Models with Python : Implement Probabilistic Models for Learning Complex Data Sequences Using the Python Ecosystem?

Author

Ankur Ankan & Abinash Panda [Ankan, Ankur & Panda, Abinash]

Publisher

Packt Publishing - ebooks Account

Published

2018

Language

EN

ISBN

9781788622226

Category

nonfiction

Subjects

Science, Computer Science, Stem