Price $499.00

Course Features
Language: English
18 Hours Instructor-Led Training
Flexible Learning
Training Completion Certificate

Graphical models training certification is designed to develop professionals knowledge in graphic models, fundamental of graphic models, probabilistic theories, types of graphic models such as Bayesian and MarKov networks, Bayesian and MarKov networks representation, and concepts of Bayesian and MarKov networks. The course covers theories and assumption of decision making, and learning of graphical models. Further,this course will help you to manage uncertain graphical models, decision theory, graphical model inference, and graphical modes.

For more information you can visit ProICT Training website and enroll yourself in this course.

Who should pursue this course?

Graphical models certification training course can ideal for professionals who are interested in data science field and have some basic knowledge of machine learning or graphical modelling.

The graphical models certification covers following topics:

  • Introduction to graphical model
  • Bayesian network
  • Markov’s networks
  • Inference
  • Model learning

Objectives of the Graphical Models Certification Training

The graphical models certification course consist some objectives which are given below:

  • Write python spam detection code
  • Latent semantic indexing in python
  • Write python sentiment analysis code
  • Write python with your own article spinner
  • Use n-Gram models to model and analyze the bag of words.

Benefits to take Graphical Models Certification Training

Through this AI Certification course, you will able to do:

  • Learn techniques to evaluate common file types
  • Learn the basics of natural language processing in python library: NLTK
  • Implement a bag of words modeling and Tokenization of text
  • Learn latent semantic analysis and its usage
  • Work with real-time data
  • Learn to use I python notebooks


The graphical model certification training certification have some requirements, a student should aware of:

  • probability theories, statistic, python, and fundamentals of AI and ML
  • Basic knowledge of AI technology.

Duration of the course

The duration of this course is 18 hours.


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