Learning Introduction to Probabilistic Knowledge Graphs AI-Driven Inference and Real-World Applications

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Free Download Introduction to Probabilistic Knowledge Graphs AI-Driven Inference and Real-World Applications
Released: 01/2026
Duration: 5h | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 569 MB
Level: Beginner | Genre: eLearning | Language: English
Probabilistic knowledge graphs (PKGs) are transforming the way data is structured and analyzed in AI and machine learning. In this course, AI engineer and author Vaibhava Lakshmi Ravideshik introduces the fundamentals of knowledge graphs and shows how probability theory helps manage uncertainty in data. Through hands-on examples, learn how to construct PKGs, integrate probabilistic reasoning, and apply inference techniques like Markov Chain Monte Carlo (MCMC). Explore real-world applications in decision-making, risk assessment, and predictive modeling, and gain insights into scaling challenges and emerging trends shaping the future of PKGs.

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Introduction to Probabilistic Knowledge Graphs: AI-Driven Inference and Real-World Applications Online Class | LinkedIn Learning, formerly Lynda.com

Explore probabilistic knowledge graphs (PKGs)—their structure, probability theory, inference methods, and real-world applications.

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