Machine Learning and AI Foundations: Causal Inference and Modeling
Machine Learning and AI Foundations: Causal Inference and Modeling
Duration: 2h 51m 14s | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 481 MB
Genre: eLearning | Language: English
Duration: 2h 51m 14s | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 481 MB
Genre: eLearning | Language: English
This course with instructor Keith McCormick provides an introduction to some advanced techniques in causal inference and causal modeling. It builds upon a foundation in Keith’s course, Machine Learning and AI Foundations: Prediction, Causality, and Statistical Inference. Keith focuses the course on three major topics: The power of experiments (and the reality that they aren't always available as an option); the Bayesian statistic philosophy and approach and when it's a good choice; and an introduction to causal modeling with techniques like structural equation modeling and Bayesian networks. Join Keith in this course to learn about these advanced techniques and what makes them both powerful and interesting.
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