Probabilistic Programming with Python and Julia We will cover all major fields of Probabilistic Programming: Distributions, Markov Chain Monte Carlo, Gaussian Mixture Models, Bayesian Linear Regression
What you’ll learn
- Introduction to probabilistic programming
- Bayesian statistics
- Markov Chain Monte Carlo
- Gaussian Mixture Models
- Bayesian Logistic Regression
- Bayesian Linear Regression
- Elementary understanding of statistics
You want to know and to learn one of the top 10 most influencial algorithms of the 20th century? Then you are right in this course. We will cover many powerful techniques from the field of probabilistic programming. This field is fast-growing, because these technique are getting more and more famous and proof to be efficient and reliable.
We will cover all major fields of Probabilistic Programming: Distributions, Markov Chain Monte Carlo, Gaussian Mixture Models, Bayesian Linear Regression, Bayesian Logistic Regression, and hidden Markov models.
For each field, the algorithms are shown in detail: Their core concepts are presented in 101 lectures. Here, you will learn how the algorithm works. Then we implement it together in coding lectures. These are available for Python and Julia. With this knowledge you can clearly identify a problem at hand and develop a plan of attack to solve it.
Mastering this course will enable you to understand the concepts of probabilistic programming and you will be able to apply this in your private and professional projects.Who this course is for:
- Python and Julia users who like to learn probabilistic programming
I’m a wind turbine engineer with a strong focus on data engineering, processing, and analysis over the entire life cycle of a wind turbine. Next to my engineering degree, I also hold an degree in Data Science.
Facilitating automated data analysis and integration of various data sources while acting as a link between engineering and IT is what