Probabilistic Graphical Models Courses
Probabilistic Graphical Models courses can help you learn Bayesian networks, Markov random fields, and inference algorithms. You can build skills in modeling uncertainty, reasoning under uncertainty, and making predictions based on incomplete data. Many courses introduce tools like TensorFlow Probability and PyMC3, which are used for implementing these models and performing complex calculations, enabling you to apply your knowledge to real-world data analysis and machine learning tasks.
Popular Probabilistic Graphical Models Courses and Certifications
Stanford University
Skills you'll gain: Bayesian Network, Applied Machine Learning, Decision Intelligence, Bayesian Statistics, Graph Theory, Machine Learning Algorithms, Probability Distribution, Network Model, Statistical Modeling, Machine Learning Methods, Markov Model, Decision Support Systems, Machine Learning, Unsupervised Learning, Probability & Statistics, Network Analysis, Statistical Inference, Model Training, Statistical Machine Learning, Model Optimization
Stanford University
Skills you'll gain: Bayesian Network, Decision Intelligence, Bayesian Statistics, Graph Theory, Probability Distribution, Network Model, Statistical Modeling, Markov Model, Decision Support Systems, Probability & Statistics, Network Analysis, Dependency Analysis
Stanford University
Skills you'll gain: Bayesian Network, Applied Machine Learning, Machine Learning Algorithms, Bayesian Statistics, Machine Learning Methods, Markov Model, Statistical Machine Learning, Machine Learning, Network Model, Unsupervised Learning, Model Training, Probability Distribution, Model Optimization, Statistical Methods, Probability & Statistics, Algorithms
Skills you'll gain: Bayesian Network, Bayesian Statistics, Network Model, Artificial Intelligence and Machine Learning (AI/ML), Predictive Modeling, Markov Model, Statistical Modeling, Statistical Inference, Graph Theory, Probability & Statistics, Sampling (Statistics), Algorithms
Stanford University
Skills you'll gain: Bayesian Network, Machine Learning Methods, Statistical Inference, Markov Model, Statistical Machine Learning, Graph Theory, Sampling (Statistics), Applied Machine Learning, Statistical Methods, Probability & Statistics, Algorithms, Probability Distribution, Machine Learning Algorithms
Johns Hopkins University
Skills you'll gain: Network Analysis, R Programming, Statistical Analysis, Regression Analysis, Statistical Modeling, Statistical Methods, Combinatorics, Bayesian Network, Applied Machine Learning, Statistical Hypothesis Testing, Statistical Programming, Data Analysis, R (Software), Probability, Probability Distribution, Probability & Statistics, Bayesian Statistics, Social Network Analysis, Simulations, Statistical Software
What brings you to Coursera today?
DeepLearning.AI
Skills you'll gain: Descriptive Statistics, Bayesian Statistics, Statistical Hypothesis Testing, Probability & Statistics, Sampling (Statistics), Statistical Methods, Probability Distribution, Probability, Statistical Inference, Statistics, A/B Testing, Statistical Analysis, Statistical Machine Learning, Data Science, Exploratory Data Analysis, Correlation Analysis, Histogram, Statistical Visualization, Box Plots
Skills you'll gain: Shiny (R Package), PyTorch (Machine Learning Library), Dashboard, Dashboard Creation, Python Programming, Interactive Data Visualization, Data Visualization, Data Visualization Software, Pandas (Python Package), Image Analysis, Applied Machine Learning, AI Workflows, Machine Learning Methods, Data Science, Computer Programming, Web Frameworks, Application Development, UI Components, Web Development Tools, User Interface (UI)
Skills you'll gain: Descriptive Statistics, A/B Testing, Classification And Regression Tree (CART), Dashboard, Dashboard Creation, Model Evaluation, Model Deployment, Data-Driven Decision-Making, Risk Analysis, Histogram, Statistical Inference, Descriptive Analytics, Simulations, Predictive Modeling, Regression Analysis, Data Visualization, MLOps (Machine Learning Operations), Decision Making, Decision Tree Learning, Keras (Neural Network Library)
Skills you'll gain: Generative AI, Generative Model Architectures, Generative Adversarial Networks (GANs), OpenAI, Hugging Face, Large Language Modeling, Deep Learning
Skills you'll gain: Generative AI, Generative Model Architectures, Large Language Modeling, LLM Application, Generative Adversarial Networks (GANs), Retrieval-Augmented Generation, OpenAI, Hugging Face, OpenAI API, Multimodal Prompts, Responsible AI, AI Security, Autoencoders, Model Deployment, Fine-tuning, Application Deployment
Skills you'll gain: Descriptive Statistics, Data Analysis, Predictive Modeling, Predictive Analytics, Data Literacy, Statistical Modeling, Business Analytics, Statistical Hypothesis Testing, Exploratory Data Analysis, Data-Driven Decision-Making, Customer Analysis, Statistical Machine Learning, Data Science, Model Evaluation, Scikit Learn (Machine Learning Library), Statistical Analysis, Feature Engineering, Data Visualization, Statistical Inference, Supervised Learning
In summary, here are 10 of our most popular probabilistic graphical models courses
- Probabilistic Graphical Models:Â Stanford University
- Probabilistic Graphical Models 1: Representation:Â Stanford University
- Probabilistic Graphical Models 3: Learning:Â Stanford University
- Probabilistic Graphical Models: A Compact Introduction:Â Coursera
- Probabilistic Graphical Models 2: Inference:Â Stanford University
- Statistical Methods for Computer Science:Â Johns Hopkins University
- Probability & Statistics for Machine Learning & Data Science:Â DeepLearning.AI
- Hands-On Data Science with PyTorch & Pandas:Â Packt
- Statistical Inference & Predictive Modeling Foundations:Â Coursera
- Generative AI: Foundation Models and Platforms:Â IBM
