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Overview
Then learn the algorithms used to train, predict, and evaluate Hidden Markov Models for pattern recognition. HMMs have been used for gesture recognition in computer vision, gene sequence identification in bioinformatics, speech generation & part of speech tagging in natural language processing, and more. The Fundamentals of Probabilistic Graphical Models programme is offered by Udacity.
Course Skills
- Fundamentals of Probabilistic Graphical Models
Likelihood function • Bayesian networks • Part of speech tagging • Basic probability • Ibm watson • Viterbi algorithm • Hidden markov models • Text pre-processing • Baum-welch algorithm • Time-series analysis with ML
Programme Structure
Courses include:
- Probability
- Spam Classifier with Naive Bayes
- Bayes Nets
- Inference in Bayes Nets
- Part of Speech Tagging with HMMs
Key information
Duration
- Part-time
- 21 days
Start dates & application deadlines
You can apply for and start this programme anytime.
Language
English
Delivered
Online
Disciplines
Graphic Design View 61 other Short Courses in Graphic Design in United StatesAcademic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Scripting
- Jupyter notebooks
- Basic data structures and algorithms
- Basic descriptive statistics
- Intermediate Python
- Linear algebra
- Differential calculus
Tuition Fee
To always see correct tuition fees
-
International
249 USD/moduleTuition FeeBased on the tuition of 249 USD per module during 21 days. -
National
249 USD/moduleTuition FeeBased on the tuition of 249 USD per module during 21 days.
249$ - Monthly Subscription - Unlimited access to all Udacity programs, skills and projects
Funding
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