Unsupervised Machine Learning Hidden Markov Models in Python

Unsupervised Machine Learning Hidden Markov Models in Python

What you’ll learn

  • Understand and enumerate the various applications of Markov Models and Hidden Markov Models
  • Understand how Markov Models work
  • Write a Markov Model in code
  • Apply Markov Models to any sequence of data
  • Understand the mathematics behind Markov chains
  • Apply Markov models to language
  • Apply Markov models to website analytics
  • Understand how Google’s PageRank works
  • Understand Hidden Markov Models
  • Write a Hidden Markov Model in Code
  • Write a Hidden Markov Model using Theano
  • Understand how gradient descent, which is normally used in deep learning, can be used for HMMs

Who this course is for:

  • Students and professionals who do data analysis, especially on sequence data
  • Professionals who want to optimize their website experience
  • Students who want to strengthen their machine learning knowledge and practical skillset
  • Students and professionals interested in DNA analysis and gene expression
  • Students and professionals interested in modeling language and generating text from a model

Can I download Unsupervised Machine Learning Hidden Markov Models in Python course?

You can download videos for offline viewing in the Android/iOS app. When course instructors enable the downloading feature for lectures of the course, then it can be downloaded for offline viewing on a desktop.
Can I get a certificate after completing the course?
Yes, upon successful completion of the course, learners will get the course e-Certification from the course provider. The Unsupervised Machine Learning Hidden Markov Models in Python course certification is a proof that you completed and passed the course. You can download it, attach it to your resume, share it through social media.
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