Bayes Theorem Courses and Certifications

Steve Brunton

Bayes' Theorem for Drug Testing and Screening Analysis

Explore Bayes' theorem through practical drug testing scenarios, learning how to calculate conditional probabilities and understand sensitivity versus specificity in medical screening.

• YouTube
• 13 minutes
• Self-Paced
• Free Video

Steve Brunton

Bayes' Theorem - Understanding Posterior, Prior, and Updates in Probability

Discover the fundamental concepts of Bayes' Theorem through clear explanations and practical examples, including its application in cancer screening and modern machine learning approaches.

• YouTube
• 18 minutes
• Self-Paced
• Free Video

Probabilistic Graphical Models 1: Representation

18 ratings

Explore probabilistic graphical models, including Bayesian networks and Markov networks, to encode complex probability distributions for applications like medical diagnosis and speech recognition.

• Coursera
• 67 hours
• Self-Paced
• Paid Course

Mathematical Biostatistics Boot Camp 1

17 ratings

Intense immersion into mathematical biostatistics, covering probability, expectations, distributions, confidence intervals, and more. Ideal for students with college-level math and calculus knowledge.

• Coursera
• 13 hours
• Self-Paced
• Paid Course

Bayesian Statistics

12 ratings

Learn to apply Bayesian methods for statistical inference, decision-making, and regression. Update prior probabilities, make optimal decisions, and implement model averaging using R software.

• Coursera
• 1 day 10 hours 53 minutes
• Self-Paced
• Paid Course

Introduction to Probability and Data with R

6 ratings

Learn sampling, data exploration, and basic probability using R. Gain practical skills in statistical analysis through hands-on exercises and a final project.

• Coursera
• 14 hours 35 minutes
• Self-Paced
• Paid Course

Great Learning

Role of Mathematics in Data Science and How to Learn - Data Science

Explore the essential mathematical concepts for data science, including statistics, probability, and normal distribution, with practical examples and problem-solving exercises.

• YouTube
• 1 hour
• Self-Paced
• Free Video

UofU Data Science

Bayes Rule and Its Applications in Machine Learning - Lecture 5

Master Bayes' Rule fundamentals through practical examples and discover its crucial applications in machine learning for calculating model probabilities from data.

• YouTube
• 1 hour 18 minutes
• Self-Paced
• Free Video

Great Learning

Probability and Statistics - Bayes Theorem Probability - Normal Distribution Probability

Probability Theory and Statistics are very essential when it comes to data science or predictive modeling as they form the basis for decision making. Thus it becomes, extremely important for a data scientist to be good with probability.

• YouTube
• 2 hours 28 minutes
• Self-Paced
• Free Video

Coding Train

Coding Challenge 187 - Bayes Theorem

Master Bayes' theorem and build a Naive Bayes text classifier in JavaScript with p5.js, implementing word frequency analysis, Laplacian smoothing, and sentiment classification.

• YouTube
• 54 minutes
• Self-Paced
• Free Video

Hyperparameter Tuning in Python

Learn techniques for automated hyperparameter tuning in Python, including Grid, Random, and Informed Search.

• DataCamp
• 4 hours
• Self-Paced
• Free Trial Available

Bayesian Data Analysis in Python

Learn all about the advantages of Bayesian data analysis, and apply it to a variety of real-world use cases!

• DataCamp
• 4 hours
• Self-Paced
• Free Trial Available

NPTEL-NOC IITM

Bayes' Theorem - Probability and Random Variables

Explore the fundamentals of Bayes' Theorem, including measurable functions, random variables, distribution functions, and probability mass functions.

• YouTube
• 36 minutes
• Self-Paced
• Free Video

Stochastic Maps and Bayes' Theorem

Explore categorical probability theory through diagrammatic representations, focusing on stochastic maps, conditional probabilities, and Bayes' theorem.

• YouTube
• 30 minutes
• Self-Paced
• Free Video

Probabilistic Graphical Models 3: Learning

304 ratings at Coursera

Learn to build probabilistic graphical models from data, covering parameter estimation, structure learning, and handling incomplete data. Explore advanced techniques for both directed and undirected models.

• Coursera
• 66 hours
• Self-Paced
• Paid Course