Description for Probabilistic Graphical Models Specialization
Level: Advanced
Certification Degree: Yes
Languages the Course is Available: 22
Offered by: On Coursera provided by Stanford
Duration: 4 months at 10 hours a week
Schedule: Flexible
Pricing for Probabilistic Graphical Models Specialization
Use Cases for Probabilistic Graphical Models Specialization
FAQs for Probabilistic Graphical Models Specialization
Reviews for Probabilistic Graphical Models Specialization
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Probabilistic Graphical Models Specialization
Gain expertise in deploying and managing LLMs on Azure, optimizing interactions with Semantic Kernel, and applying Retrieval Augmented Generation (RAG) techniques.
Gain expertise in deploying and managing LLMs on Azure, optimizing interactions with Semantic Kernel, and applying Retrieval Augmented Generation (RAG) techniques.
Modern and Practical Statistical Thinking for All. Utilize Python for statistical visualization, inference, and modeling.
In addition to addressing parameter estimation and structure learning, this course covers learning probabilistic graphical models from data and contains practical programming tasks for practical use.
Featured Tools
A brief overview of the material covered in this course is that it teaches students how to use logistic regression and the XG-Boost algorithm in machine learning to forecast employee turnover.
The topics of this AI course include the optimization of policies in reinforcement learning, the utilization of dimensionality reduction in unsupervised learning, and the classification and definition of constraints in supervised learning.
Create a final presentation to evaluate peer projects, train neural networks for regression and classification, and develop Python-based recommender systems. Additionally, employ KNN, PCA, and collaborative filtering.
Discover the process of identifying machine learning model types, training and deploying predictive models using Azure Machine Learning's automated capabilities, developing regression, classification, and clustering models with Azure Machine Learning Designer, and deploying models seamlessly without scripting.
This course offers practical information regarding the ethical implications, applications, and categories of machine learning.