Conditional Probability Courses and Certifications

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

Practicing Statistics Interview Questions in Python

Prepare for your next statistics interview by reviewing concepts like conditional probabilities, A/B testing, the bias-variance tradeoff, and more.

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

Foundations of Probability in Python

Learn fundamental probability concepts like random variables, mean and variance, probability distributions, and conditional probabilities.

• DataCamp
• 5 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

Advanced Probability: Uncertainty in Data

Develop a better intuition for advanced probability, risk assessment, and simulation techniques to make data-driven business decisions with confidence.

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

Steve Brunton

The Law of Total Probability - Understanding Conditional Probability Fundamentals

Master the Law of Total Probability through clear explanations and practical examples, understanding its fundamental role in probability theory and statistical analysis.

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

AnalystPrep

Probability Trees and Conditional Expectations for Investment Analysis

Master probability trees, conditional expectations, and Bayes' formula for investment analysis. Calculate expected values and apply statistical concepts to solve complex investment problems.

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

Gautam Varde

Conditional Probability and Total Probability - Engineering Mathematics

Explore conditional and total probability concepts with practical problems, enhancing your understanding of these crucial engineering mathematics topics.

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

ACM SIGPLAN

Lilac: A Modal Separation Logic for Conditional Probability

Explore Lilac, a novel separation logic for probabilistic programs, introducing a modality for conditional probability reasoning and validating its effectiveness through formal verification examples.

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

Centre for Networked Intelligence, IISc

Independence in Probability - Law of Total Probability and Conditional Independence

Explore independence in probability through the Law of Total Probability, conditional probability, and conditional independence with practical examples and proofs.

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

Institute for Pure & Applied Mathematics (IPAM)

Conditional Probability Density Functional Theory - IPAM at UCLA

Explore conditional probability density functional theory for electronic structure calculations, addressing challenges in DFT and offering insights into ground-state correlations and warm dense matter simulations.

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

Gautam Varde

Two-Dimensional Conditional Probability Distribution - Advanced Mathematics

Explore 2D conditional probability distribution with numerical examples, essential for engineering and science students in probability and statistics.

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

An Intuitive Introduction to Probability

1884 ratings at Coursera

Intuitive introduction to probability theory, covering key concepts and practical applications. Gain tools to handle uncertainty in daily life through engaging modules and exercises.

• Coursera
• 1 day 5 hours 34 minutes
• Self-Paced
• Paid Course

Introduction to Probability and Statistics for Machine Learning

Master essential probability and statistics concepts crucial for machine learning, from basic probability and descriptive statistics to hypothesis testing and linear regression analysis.

• CodeSignal
• 3 hours
• Self-Paced
• Free Certificate

Probabilistic Graphical Models 3: Learning

305 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