Search result for Probabilistic models Online Courses & Certifications
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Natural Language Processing
by Luis Serrano , Jay Alammar , Arpan Chakraborty , Dana Sheahen- 0.0
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3 Months
Build probabilistic and deep learning models, such as hidden Markov models and recurrent neural networks, to teach the computer to do tasks such as speech recognition, machine translation, and more! Build models using probabilistic and deep learning techniques and apply them to speech recognition, machine translation, and more!...
Probabilistic Deep Learning with TensorFlow 2
by Dr Kevin Webster- 4.7
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Approx. 53 hours to complete
Welcome to this course on Probabilistic Deep Learning with TensorFlow! You will learn how to develop probabilistic models with TensorFlow, making particular use of the TensorFlow Probability library, which is designed to make it easy to combine probabilistic models with deep learning. Welcome to Probabilistic Deep Learning with TensorFlow 2 Probabilistic layers and Bayesian neural networks...
Text Mining and Analytics
by ChengXiang Zhai- 4.5
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Approx. 33 hours to complete
7 Topic Mining and Analysis: Probabilistic Topic Models 1 Probabilistic Topic Models: Mixture of Unigram Language Models 2 Text Clustering: Generative Probabilistic Models Part 1 3 Text Clustering: Generative Probabilistic Models Part 2 4 Text Clustering: Generative Probabilistic Models Part 3 9 Text Categorization: Generative Probabilistic Models 5 Contextual Text Mining: Contextual Probabilistic Latent Semantic Analysis...
Probabilistic Graphical Models 1: Representation
by Daphne Koller- 4.6
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Approx. 67 hours to complete
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. Flow of Probabilistic Influence Template Models for Bayesian Networks Overview of Template Models Temporal Models - DBNs Temporal Models - HMMs Plate Models...
Probabilistic Graphical Models 2: Inference
by Daphne Koller- 4.6
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Approx. 38 hours to complete
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. Inference in Temporal Models Inference in Temporal Models Inference in Temporal Models...
Probabilistic Graphical Models 3: Learning
by Daphne Koller- 4.6
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Approx. 66 hours to complete
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. Learning in Parametric Models Learning Undirected Models Maximum Likelihood for Log-Linear Models...
Fundamentals of Quantitative Modeling
by Richard Waterman- 4.6
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Approx. 8 hours to complete
3 How Models Are Used in Practice Module 1: Introduction to Models Quiz Module 2: Linear Models and Optimization 1 Introduction to Linear Models and Optimization Module 2: Linear Models and Optimization Quiz Module 3: Probabilistic Models 1 Introduction to Probabilistic Models 2 Examples of Probabilistic Models Module 3: Probabilistic Models Quiz...
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Machine Learning
by John W. Paisley- 0.0
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12 Weeks
probabilistic versus non-probabilistic modeling We will cover three fundamental problems of unsupervised learning: data clustering, matrix factorization, and sequential models for order-dependent data. Some applications of these models include object recommendation and topic modeling. Probabilistic versus non-probabilistic viewpoints...
$249
Introduction to Probability: Part II – Inference & Processes
by John Tsitsiklis , Patrick Jaillet , Qing He , Jimmy Li- 0.0
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16 Weeks
Learn how to use probability theory to develop the basic elements of statistical inference and important random process models Probabilistic modeling and the related field of statistical inference are the keys to analyzing data and making scientifically sound predictions. Probabilistic models use the language of mathematics. Basic random process models (Bernoulli, Poisson and Markov) and their main properties...
$75
Natural Language Processing with Probabilistic Models
by Younes Bensouda Mourri , Łukasz Kaiser , Eddy Shyu- 4.7
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Approx. 28 hours to complete
Part of Speech Tagging and Hidden Markov Models Hidden Markov Models Hidden Markov Models Autocomplete and Language Models...
