Search result for Probability distributions Online Courses & Certifications
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Managing Uncertainty in Marketing Analytics
by David Schweidel- 4.3
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Approx. 11 hours to complete
Topics include the development and application of Monte Carlo simulations, and the use of probability distributions to characterize uncertainty. Randomness and Probability Applications of Randomness & Probability Randomness & Probability Excel Demonstration Using Probability Distributions to Model Uncertainty Steps in Using Probability Functions Normal Distribution & Other Probability Models...
Data Science Fundamentals for Data Analysts
by Emma Freeman , Mark Roepke- 0.0
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Approx. 19 hours to complete
An Introduction to Probability Basic Rules of Discrete Probability Discrete Probability Lab Intro Statistics and Probability Review An Introduction to Probability Distributions Discrete Probability Distributions Discrete Probability Distributions Applications Probability Distribution Lab Intro Continuous Probability Distributions Probability Distribution Review Outlier Detection with Probability Distributions Discrete Probability Lab Discrete Probability Lab Probability Distributions...
Statistics for Data Science and Analytics
by Analytics Leap- 3.8
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4.5 hours on-demand video
The second part of the course is concerned with the basics of probability: calculating probabilities, probability distributions and sampling distributions. Discrete and Continuous Probability Distributions In the fourth module, we will discuss Probability Distributions and show the difference between a continuous and discrete distributions, using the Normal and Binomial distributions as key examples....
$11.99
AI Workflow: Data Analysis and Hypothesis Testing
by Mark J Grover , Ray Lopez, Ph.D.- 4.2
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Approx. 11 hours to complete
You will learn techniques of estimation with probability distributions and extending these estimates to apply null hypothesis significance tests. Employ common distributions to answer questions about event probabilities Business Scenarios and Probability...
Business Statistics with Excel
by Diego Fernandez- 3.5
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4.5 hours on-demand video
Approximate population mean two tails, right tail and population proportion left tail statistical inference tests probability values. Estimate paired populations means two tails statistical inference test probability value. Next, you’ll define probability distributions. Then, you’ll define theoretical and empirical probability distributions. After that, you’ll define continuous random variable and continuous probability distribution....
$12.99
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. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more....
Probability and Statistics IV: Confidence Intervals and Hypothesis Tests
by David Goldsman- 0.0
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4 Weeks
We then formulate and interpret confidence intervals for a variety of probability distributions and their parameters. Finally, we formulate and interpret hypothesis tests for a variety of probability distributions and their parameters. Formulate and interpret confidence intervals for a variety of probability distributions and their parameters Formulate and interpret hypothesis tests for a variety of probability distributions and their parameters...
$199
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Mathematical Biostatistics Boot Camp 1
by Brian Caffo, PhD- 4.5
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Approx. 13 hours to complete
This class presents the fundamental probability and statistical concepts used in elementary data analysis. Set Notation and Probability Probability...
Probabilistic Deep Learning with TensorFlow 2
by Dr Kevin Webster- 4.7
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Approx. 53 hours to complete
You will learn how probability distributions can be represented and incorporated into deep learning models in TensorFlow, including Bayesian neural networks, normalising flows and variational autoencoders. The additional prerequisite knowledge required in order to be successful in this course is a solid foundation in probability and statistics. TensorFlow Distributions The TensorFlow Probability library...
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. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more....
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