Graphical Models Certification Training
Access Duration - 365 Days
4.5( 2 REVIEWS )
130 STUDENTS
What Will I Learn?
Learn the fundamentals of Graphical Models
Gain an introduction to inference
Learn the types of Graphical Models
Learn how to deal with complexity and uncertainty using Graphical Models
Overview
Use graphical models to simplify machine learning! Graphical Models Certification Training is designed to teach individuals the fundamentals of Graphical Models, concepts, types and representations. You will learn about directed graphs (Bayesian Networks) and undirected graphs (Markov’s Networks). These graphs are used in machine learning for diagnostics, speech recognition and intelligent systems. The course will focus on graph theory and how they can be applied and gain a comprehensive understanding of graph model structure and its parameters.
Graphical Models Certification Training will provide a wealth of knowledge on graph theory and probability theory to ensure learners gain a complete understanding of the key modules explored throughout the course.
Graphical Models Certification Training will also teach individuals how to use Graphical Models for better learning and decision making. Qualifying in this course will set you in the right direction, giving you the opportunity to open the door to new and exciting job roles in the artificial intelligence industry.
Why You Should Consider Taking this Course at Global Edulink?
Global Edulink is a leading online provider for several accrediting bodies, and provides learners the opportunity to take this exclusive course awarded by CPD. At Global Edulink, we give our fullest attention to our learners’ needs and ensure they have the necessary information required to proceed with the Course. Learners who register will be given excellent support, discounts for future purchases and be eligible for a TOTUM Discount card and Student ID card with amazing offers and access to retail stores, the library, cinemas, gym memberships and their favourite restaurants.
- Access Duration
- Who is this Course for?
- Entry Requirement
- Method of Assessment
- Certification
- Awarding Body
- Career Path & Progression
The course will be directly delivered to you, and you have 12 months access to the online learning platform from the date you joined the course. The course is self-paced and you can complete it in stages, revisiting the lessons at any time.
This course is recommended for individuals interested in data science, machine learning and artificial intelligence,
- Data Scientists
- Researchers
- AI Engineers
- Python Programmers
- Developers
- Learners should be over the age of 16, and have a basic understanding of English, ICT and numeracy.
- A sound educational background is recommended
- Knowledge of Python theories is recommended
- Understanding of Machine Learning (ML) and Artificial Intelligence (AI)
In order to qualify in the course ‘Graphical Models Certification Training’ successfully, learners will take an online test with each module being rounded off with multiple-choice questions. This online test is marked automatically, so you will receive an instant grade and know whether you have passed the course.
Upon the successful completion of the course, you will be awarded the Graphical Models Certification Training by CPD.
CPD is a leading awarding body in the United Kingdom that meets an excellent standard of high quality education. CPD is committed towards the enhancement of proficiency and personal skills in order to develop learners’ skills and abilities. CPD ensures that both practical and academic qualifications assist individuals to re-skill or up-skill and maintain a competitive advantage in their chosen industry.
Graphical Models Certification Training will improve your candidature for a number of jobs in Research and AI. You can study further courses in the same field and enhance your academic expertise in machine learning and artificial intelligence, and add this as a valuable skillset on your resume. Your skills will be recognised by leading organisations that give you the opportunity to land a well-paying job. Given below are job titles you can compete for, along with the average UK salary per annum according to https://www.glassdoor.com.
- Data Scientist - £70,000 (Approximately)
- Research Analyst - £49,000 (Approximately)
- AI Software Engineer - £70,000 (Approximately)
Key Features
Gain an Accredited UK Qualification
Access to Excellent Quality Study Materials
Personalised Learning Experience
Support by Phone, Live Chat, and Email
Eligible for TOTUM Discount Card
UK Register of Learning Providers Reg No : 10053842Course Curriculum
| 1: Introduction to Graphical Model | |||
| Why do we need Graphical Models? | |||
| Introduction to Graphical Model | |||
| How does Graphical Model help you deal with uncertainty and complexity? | |||
| Types of Graphical Models | |||
| Graphical Modes | |||
| Components of Graphical Model | |||
| Representation of Graphical Models | |||
| Inference in Graphical Models | |||
| Learning Graphical Models | |||
| Decision theory | |||
| Applications | |||
| 2: Bayesian Network | |||
| What is Bayesian Network? | |||
| Advantages of Bayesian Network for data analysis | |||
| Bayesian Network in Python Examples | |||
| Independencies in Bayesian Networks | |||
| Criteria for Model Selection | |||
| Building a Bayesian Network | |||
| 3: Markov’s Networks | |||
| Example of a Markov Network or Undirected Graphical Model | |||
| Markov Model | |||
| Markov Property | |||
| Markov and Hidden Markov Models | |||
| The Factor Graph | |||
| Markov Decision Process (MDP) | |||
| Decision Making under Uncertainty | |||
| Decision Making Scenarios | |||
| 4: Inference | |||
| Inference | |||
| Complexity in Inference | |||
| Exact Inference | |||
| Approximate Inference | |||
| Monte Carlo Algorithm | |||
| Gibb’s Sampling | |||
| Inference in Bayesian Networks | |||
| 5: Model Learning | |||
| General Ideas in Learning | |||
| Parameter Learning | |||
| Learning with Approximate Inference | |||
| Structure Learning | |||
| Model Learning: Parameter Estimation in Bayesian Networks | |||
| Model Learning: Parameter Estimation in Markov Networks | |||
Raylee Wells
The training met a professional standard and explained the fundamentals of Graphical Models. As someone who aspires to work in Data Science, this course was exactly what I was looking for.
Chris Hart
Gaining a recognised qualification on this course has certainly improved my prospects in landing a well-paying job in the industry.