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Looking for a study partner in Probabilistic Graphical Models and/or Deep Learning

11th June, 2020
Last date to join: 30th June, 2020

I am working through the Probalistic Graphical Models course. I’m also willing to do a coursera on Deep Learning and any NLP course. Let me know if you are interested.

Novice English

Description

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems. Applied Learning Project Through various lectures, quizzes, programming assignments and exams, learners in this specialization will practice and master the fundamentals of probabilistic graphical models. This specialization has three five-week courses for a total of fifteen weeks. Read more

Probabilistic Graphical Models Specialization

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Microcredentials
Coursera
Free to Audit
4 months at 10 hours a week
Advanced
Paid Certificate

Looking for a study partner in Probabilistic Graphical Models and/or Deep Learning

11th June, 2020
Last date to join: 30th June, 2020
Start Learning
Affiliate notice

I am working through the Probalistic Graphical Models course. I’m also willing to do a coursera on Deep Learning and any NLP course. Let me know if you are interested.

Novice English

  • Type
    Microcredentials
  • Provider
    Coursera
  • Pricing
    Free to Audit
  • Duration
    4 months at 10 hours a week
  • Difficulty
    Advanced
  • Certificate
    Paid Certificate

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems. Applied Learning Project Through various lectures, quizzes, programming assignments and exams, learners in this specialization will practice and master the fundamentals of probabilistic graphical models. This specialization has three five-week courses for a total of fifteen weeks. Read more

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Moocable is a community where you can find study partners, mentors, or people to collaborate on projects. It's designed for people who want to upskill, but struggle with self-learning. Users often post about their skills, goals, and what they're looking to learn or work on, and others can respond to form partnerships or groups. You can join our community

30th June, 2020

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