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Take a deep dive into Bayesian modeling.
Bayesian statistics is a theory based on Bayesian probability, an idea that has revolutionized many industries by dealing with probability distributions in a different way. The Bayesian interpretation of probability expresses probability as a degree of belief in an event. In this 2-day deep dive into Bayesian statistics, you’ll discover how to solve multi-armed bandits using techniques such as Markov chain Monte Carlo and Variational Inference, and much more.
This training is for you if…
- You already understand basic statistics.
- You are interested in learning about Bayesian statistics and how it differs from the frequentist approach.
- You want to solve real-world problems by translating them into probabilistic models.
This training is not for you if…
- You are missing the fundamentals of statistics.
- You are NOT interested in improving the way you work with statistics.
- You have never used Python before.
Clients we've helped
What you'll learn
- The Bayesian interpretation of probability: what it is and how it differs from classical interpretations
- Prior, likelihood, and posterior distributions
- Application of Bayes’ Theorem to solve probabilistic problems
- Different distributions
- The difference between probability mass and density functions
Bayes' Theorem in Practice
- Translate a real-world problem into a probabilistic model
- Finding the posterior distribution in practice
- When to use Markov chain Monte Carlo and when to use Variational Inference
- The Random Walk Metropolis Hastings algorithm and how it works
- Fundamentals: Bayes’ Theory
- From Bayes’ Theorem to Bayesian Data Analysis
- The Bayesian’ Paradigm
- Markov chain Monte Carlo with PyMC3
- Variational Inference: Big Data Bayesian Data Analysis
Multi-armed bandit problems (like for example A/B testing) can be solved by using Bayesian modeling. Participants will be presented with the simulation environment for Multi-armed bandits and encouraged to code a Bayesian decision-making algorithm. A perfect opportunity to creatively brainstorm and learn more about practical applications of Bayesian theory and effectively balancing the exploration-exploitation tradeoff
After the training you will be able to:
- Understand the theory of Bayesian Statistics and how it differs from the classical approach.
- Apply Bayes’ Theorem to real-world problems.
- Use PyMC to put Bayesian statistics into practice.
Data Science Learning Journey
This Bayesian Modeling training is perfect for
- Data Scientists who know Machine Learning and want to learn about Bayesian statistics.
- This training is especially suited for Data Scientists who want to go beyond the standard probability theory.
- To get the most out of this training, we advise that you have at least one year of working experience with Python.
What will you learn during the Bayesian Modeling training:
- You will understand what makes Bayesian Probability so powerful, especially compared to the traditional frequentist approach.
- You will learn how to use the PyMC for building Bayesian models.
- We will also teach you how to apply Markov Chain Monte Carlo, Variational inference and other applications of Bayesian modeling in practice.
Structured, to-the-point, good combination of theory and practical examples, very knowledgeable trainer who can explain concepts very well
It was a hands-on and tangible course. We could apply what we learned in a matter of minutes. The trainer did a great job of answering ad-hoc questions that complemented the material. We appreciated the fact that we could apply what we were taught directly to our company.
I liked every aspect of this training and would like to thank the trainers. They did an excellent job of explaining how to use Spark for data science. This is the fourth GoDataDriven training I’ve followed. All were great, but this was the best one so far.
Climbing a steep Python and Machine Learning curve in three days. This would have taken me months on my own.