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Deep Dive into Bayesian Modeling
Bayesian probability is a powerful technique that has revolutionized many industries by dealing with probability distributions in a different way. Discover techniques such as Markov chain Monte Carlo and Variational Inference, and how to solve multi-armed bandits. This 2-day training offers a deep-dive into this and much more!
This training is for you if…
You already have a good understanding of the basics of statistics.
You are interested in learning about Bayesian statistics and how it differs to the frequentist approach.
You want to solve real-world problems by translating them into probabilistic models.
This training is not for you if…
We also have a dedicated courses on A/B testing and innovating through statistical experiments.
Clients we've helped
What you'll learn
You will learn:
- The theorem that underlies Bayesian data analysis and learn to apply it to solve probabilistic problems.
- How Bayes’ Theorem can be applied to data and make yourself comfortable with the Bayesian terminology: prior distributions, likelihoods, and posterior distributions.
- How The Bayesian paradigm is fundamentally different than the (more famous) “frequentist” paradigm.
- The (practical) pros and cons of working with either the Bayesian or the frequentist approach.
- Markov chain Monte Carlo (MCMC) methods
- How Variational inference offers an alternative to MCMC that is suitable to (very) big data.
- 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
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.