vnikov,github

practical-python-ai-projects

Source code for 'Practical Python AI Projects' by Serge Kruk

practical-web-scraping-for-data-science

Source code for 'Practical Web Scraping for Data Science' by Seppe vanden Broucke and Bart Baesens

practical_rl

A course in reinforcement learning in the wild

practice-it-rust-file-manipulation-3082491

This is a LinkedIn Learning repo for Practice It: Rust File Manipulation

predictive-maintenance

Data Wrangling, EDA, Feature Engineering, Model Selection, Regression, Binary and Multi-class Classification (Python, scikit-learn)

pro-machine-learning-algorithms

Source Code for 'Pro Machine Learning Algorithms' by Kishore Ayyadevara

pro-python-best-practices

Source code for 'Pro Python Best Practices' by Kristian Rother

probabilistic-programming-and-bayesian-methods-for-hackers

aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

prog4comp_2

Resources for the 2nd edition of "Programming for Computations" by S. Linge and H.P. Langtangen

programmers-introduction-to-mathematics

Code for A Programmer's Introduction to Mathematics

programming-basics-book-python-bg

Textbook for the "Programming Basics" course @ SoftUni (Python, Bulgarian)

programming-with-data

Programming with Data: Python and Pandas 🐍📊🐍

pycon-2018-tutorial

Using pandas for Better (and Worse) Data Science

pycon-cz-2019-workshop

The Data Trinity – Practical Numpy, Pandas and Matplotlib

pycon2017

Bayesian Machine Learning, https://us.pycon.org/2017/schedule/presentation/232/

pyconus_2019-model-interpretability-tutorial

Content for the Model Interpretability Tutorial at Pycon US 2019

pydata-book

Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media

pydata_london_2018_computer_vision

These are the slides and code for my tutorial "Computer Vision: an (Un?)Expected Journey" at PyData London 2018

pydata_nyc2018-intro-to-model-interpretability

Notebook and slides for my talk at Pydata NYC 2018

pymc3_datasciencela

PyMC3 tutorial for DataScience LA (January 2017)

Personal project to compare hierarchical linear regression in PyMC3 and PyStan, as presented at http://pydata.org/london2016/schedule/presentation/30/ video: https://www.youtube.com/watch?v=Jb9eklfbDyg