Search result for Bayesian methods Online Courses & Certifications
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Forecasting Models with Excel
by Diego Fernandez- 2.6
8 hours on-demand video
Learn main forecasting models and methods from basic to expert level through a practical course with Excel. Approximate exponential smoothing methods with trend and seasonal patterns such as Holt-Winters additive, Holt-Winters multiplicative and Holt-Winters damped methods. Select exponential smoothing method with lowest Akaike, corrected Akaike and Schwarz Bayesian information loss criteria....
$14.99
Machine Learning
by Charles Isbell- 0.0
14 Weeks
statistical supervised and unsupervised learning methods Bayesian learning methods In order to ground these methods the course includes some programming and involvement in a number of projects....
$99
Statistics with R Capstone
by Merlise A Clyde , Colin Rundel , David Banks , Mine Çetinkaya-Rundel- 4.6
Approx. 6 hours to complete
A large and complex dataset will be provided to learners and the analysis will require the application of a variety of methods and techniques introduced in the previous courses, including exploratory data analysis through data visualization and numerical summaries, statistical inference, and modeling as well as interpretations of these results in the context of the data and the research question....
Introduction to Probability: Part II – Inference & Processes
by John Tsitsiklis , Patrick Jaillet , Qing He , Jimmy Li- 0.0
16 Weeks
the main tools of Bayesian inference methods an introduction to classical statistical methods Bayesian Inference: concepts and key methods...
$75
Probabilistic Graphical Models 3: Learning
by Daphne Koller- 4.6
Approx. 66 hours to complete
They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. Parameter Estimation in Bayesian Networks Maximum Likelihood Estimation for Bayesian Networks Bayesian Estimation Bayesian Prediction Bayesian Estimation for Bayesian Networks Bayesian Priors for BNs...
Fitting Statistical Models to Data with Python
by Brenda Gunderson , Brady T. West , Kerby Shedden- 4.4
Approx. 15 hours to complete
This course will introduce and explore various statistical modeling techniques, including linear regression, logistic regression, generalized linear models, hierarchical and mixed effects (or multilevel) models, and Bayesian inference techniques. Mixed effects models: Is it time to go Bayesian by default? Linear Regression Models: Notation, Parameters, Estimation Methods Logistic Regression Models: Notation, Parameters, Estimation Methods...
Probabilistic Graphical Models 1: Representation
by Daphne Koller- 4.6
Approx. 67 hours to complete
They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. Bayesian Network (Directed Models) Independencies in Bayesian Networks Bayesian Network Fundamentals Bayesian Network Independencies Template Models for Bayesian Networks Structured CPDs for Bayesian Networks...
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Forecasting Models with R
by Diego Fernandez- 3.9
5.5 hours on-demand video
Estimate exponential smoothing methods with only trend patterns such as Holt linear trend, exponential trend, Gardner additive damped trend and Taylor multiplicative damped trend methods. Approximate exponential smoothing methods with trend and seasonal patters such as Holt-Winters additive, Holt-Winters multiplicative and Holt-Winters damped methods. Next, you’ll evaluate simple methods forecasting accuracy through scale-dependent and scale-independent error metrics....
$12.99
Practical Predictive Analytics: Models and Methods
by Bill Howe- 4.1
Approx. 7 hours to complete
Use resampling methods to make clear and bulletproof statistical arguments without invoking esoteric notation Explain and apply a core set of classification methods of increasing complexity (rules, trees, random forests), and associated optimization methods (gradient descent and variants) Explain and apply a set of unsupervised learning concepts and methods Comparing Classical and Resampling Methods Bayesian vs. Motivation for Bayesian Approaches...
Mathematics & Statistics of Machine Learning & Data Science
by Cinnamon TechX- 4.2
11 hours on-demand video
Shallow learning refers to machine learning methods that plateau at a certain level of performance when you add more examples and training data to the network. Bayesian Decision Theory Parametric Methods Multivariate Methods...
$12.99