Probabilistic Programming with Python and Julia

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Bert Gollnick,Sebastian Kaus

2:39:24

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1 - Introduction

1 - Course Overview.mp4

02:49

2 - Bayesian Statistics.mp4

11:45

3 - Distributions Introduction.mp4

01:05

4 - Distributions Uniform Distribution.mp4

04:47

5 - Distributions Normal Distribution.mp4

04:42

6 - Distributions Binomial Distribution.mp4

02:09

7 - Distributions Poisson Distribution.mp4

01:31

8 - Monte Carlo Markov Chain.mp4

08:10

2 - Samplers

9 - Metropolis Hastings Sampling 101.mp4

06:52

10 - Metropolis Hastings Sampling Interactive 1.mp4

03:56

11 - Metropolis Hastings Sampling Interactive 2.mp4

03:16

12 - Metropolis Hastings Sampling Interactive 3.mp4

02:57

3 - Workspace Preparation

13 - Julia.mp4

04:12

13 - atom download.zip

13 - julia download.zip

13 - juno download.zip

14 - Python.mp4

08:08

14 - anaconda download for python.zip

4 - Gaussian Mixture Models

15 - GMM 101.mp4

05:46

16 - Kmeans 101.mp4

07:23

17 - GMM Coding Julia.mp4

10:35

17 - gmm.jl.zip

18 - GMM Coding Python.mp4

05:40

18 - gaussianmixturemodel-vs-kmeans.ipynb.zip

5 - Bayesian Linear Regression

19 - Bayesian Linear Regression 101.mp4

06:58

20 - Bayesian Linear Regression Coding Julia.mp4

09:43

20 - Starwars.csv

20 - linearregression.jl.zip

21 - Bayesian Linear Regression Coding Python.mp4

15:26

21 - Starwars.csv

21 - linearregression.ipynb.zip

6 - Bayesian Logistic Regression

22 - Bayesian Logistic Regression 101.mp4

09:41

23 - Bayesian Logistic Regression Coding Julia.mp4

13:27

23 - Cryotherapy.CSV

23 - logisticregression.jl.zip

24 - Bayesian Logistic Regression Coding Python.mp4

08:26

24 - Starwars.csv

24 - logisticregression.ipynb.zip

7 - Bonus

25 - Congratulation and Thank you.html

26 - Bonus lecture.html

Description

Introduction and simple examples to start into probabilistic programming

What You'll Learn?

Introduction to probabilistic programming
Bayesian statistics
Markov Chain Monte Carlo
Gaussian Mixture Models
Bayesian Logistic Regression
Bayesian Linear Regression

Who is this for?

Python and Julia users who like to learn probabilistic programming

What You Need to Know?

Python

Julia

Elementary understanding of statistics

More details

Description
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

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Python

Bert Gollnick

Instructor's Courses

I am a hands-on Data Scientist with a lot of domain knowledge on Renewable Energies, especially Wind Energy.Currently I work for a leading manufacturer of wind turbines. I provide trainings on Data Science and Machine Learning with R and Python since many years.I studied Aeronautics, and Economics. My main interests are Machine Learning, Data Science, and Blockchain.

Sebastian Kaus

Instructor's Courses

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 I do in my professional life, in my private life, I like photography, the ocean and sailing.

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