Awesome Python Videos

This repository contains videos from almost all the python conferences across the world. You'll find videos related to python, machine learning and data science and videos from some of the python expert {pythonista} who teach us how to use python more effectively.
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Table of contents

• Most Favourite
• Python Conferences
  • PyCon
  • PyData
  • PyBay
  • SciPy
  • EuroPython
  • Meetups
• Machine Learning and Data Science
• Contributing
• Creator
• Thanks

Favourites

Informative

• Build Your Own Async (David Beazley)
• Structuring Your First Python Project
• Object Oriented Programming from scratch (four times)
• Modern Dictionaries
• Transforming Code into Beautiful, Idiomatic Python
• How to become a Data Scientist in 6 months
• High Performance Data Processing in Python
• Magic Wormhole- Simple Secure File Transfer

Python Conferences

PyCon

• PyCon 2020

  • Object Oriented Programming from scratch (four times) by Raymond Hettinger
  • Introduction to Decorators: Power Up Your Python Code by Geir Arne Hjelle
  • Python Concurrency: from beginner to pro by Santiago Basulto
  • Migration from Python 2 to 3 by Mike Müller
  • Natural Language Processing (NLP) in Python - From Zero to Hero by Keith Galli
  • Modern Python Developer's Toolkit by Sebastian Witowski
  • Why is Python slow? by Anthony Shaw
  • A careful walk through probability distributions, using Python by Eric J. Ma

• PyCon 2019

  • Practical decorators by Reuven M. Lerner
  • Design Patterns in Python for the Untrained Eye by Ariel Ortiz
  • Type hinting (and mypy) by Bernat Gabor
  • PEP 572: The Walrus Operator by Dustin Ingram
  • Thinking like a Panda: Everything you need to know to use pandas the right way by Hannah Stepanek

• PyCon 2018

  • Python Oddities Explained by Trey Hunner
  • Reinventing the Parser Generator by David Beazley
  • Dataclasses: The code generator to end all code generators by Raymond Hettinger
  • Type-checked Python in the real world by Carl Meyer

• PyCon 2017

  • A Really Gentle Introduction to Asyncio by Gregory Saunders

• PyCon 2016

  • Thinking about Concurrency by Raymond Hettinger
  • Refactoring Python: Why and how to restructure your code by Brett Slatkin
  • Magic Wormhole- Simple Secure File Transfer By Brian Warner

• PyCon 2015

  • Type Hints by Guido van Rossum
  • Beyond PEP 8 -- Best practices for beautiful intelligible code by Raymond Hettinger
  • Python Concurrency From the Ground Up by David Beazley
  • Modules and Packages by David Beazley

PyData

• PyData 2020

  • Furious & Fast Python 7: Writing Fast Python Code by James Powell
  • Making Pandas Fly by Ian Ozsvald
  • Pandas and friend by Marc Garcia

• PyData 2019

  • Objectionable Content by James Powell
  • Customizing JupyterLab using extensions by Alex Bozarth, Luciano Resende
  • ADTK: An open-source Python toolkit for anomaly detection in... by Tailai Wen
  • Machine Learning Crash Course by Samuel Taylor
  • AI pipelines powered by Jupyter notebooks by Luciano Resende, Alan Chin
  • Do You Want To Build A Forest? by Thomas J Fan
  • A gentle introduction to Pandas timeseries and Seaborn by Ian Ozsvald
  • Stars, Planets, and Python by Sara Seager
  • A/B Testing in Python by Raul Maldonado
  • Dealing With Imbalanced Classes in Machine Learning by Aditya Lahiri

• PyData 2018

  • I Just Inherited 50,000 Lines of Code! What Now? — A Practical Guide by James Powell
  • Applying Statistical Modeling & Machine Learning to Perform Time-Series Forecasting by Tamara Louie
  • Winning with Simple, even Linear, Models by Vincent Warmerdam
  • More About Generators by James Powell
  • Unit Testing for Data Scientists by Hanna Torrence
  • Cleaning and Tidying Data in Pandas by Daniel Chen
  • Train, Evaluate, Repeat: Building a Credit Card Fraud Detection System by Leela Senthil Nathan
  • Using Simpson’s Paradox to Discover Interesting Patterns in by Nazanin Alipourfard, Peter Fennell
  • High Performance Data Processing in Python by Donald Whyte
  • Making Computation Easier with Cool Numpy Tricks by Kirit Thadaka

• PyData 2017

  • Applied Data Science by Giovanni Lanzani
  • So you want to be a Python expert? by James Powell
  • Building Web-based Analysis & Simulation Platforms by James Powell
  • Top to down, left to right (Surprise talk) by James Powell
  • Advanced Metaphors in Coding with Python by James Powell
  • Time Series Forecasting using Statistical and Machine Learning Models by Jeffrey Yau
  • A Worked Example of Using Neural Networks for Time Series Prediction by Joe Jevnik

• PyData 2016

  • Python Language by Guido van Rossum
  • Memory Management in Python - The Basics by Nina Zakharenko
  • Design Principles by James Powell
  • Fizz Buzz in Tensorflow by Joel Grus
  • Learning scikit learn - An Introduction to Machine Learning in Python by Sebastian Raschka
  • Design Principles by James Powell
  • How to become a Data Scientist in 6 months by Tetiana Ivanova
  • Python and Johnny Cash by James Powell

• PyData 2015

  • Does Code Quality Really Matter by James Powell
  • Machine Learning with scikit learn by Andreas Mueller
  • Learning Data Science Using Functional Python by Joel Grus
  • Deploying scikit learn Models in Production by Rajat Arya
  • CPython Meanderings by James Powell

• PyData 2014

  • Generators Will Free Your Mind by James Powell

PyBay

• PyBay 2019

  • The Mental Game of Python by Raymond Hettinger
  • Perceiving Python Programming Paradigms by Jigyasa Grover
  • Patterns for Clean API Design by Paul Ganssle
  • What's Coming in 3 8 Assignment Expressions & More! by Adam Forsyth

• PyBay 2018

  • Preventing, Finding, and Fixing Bugs On a Time Budget by Raymond Hettinger
  • asyncio: what's next by Yury Selivanov

• PyBay 2017

  • Logging and Testing and Debugging, Oh My! by Albert Sweigart
  • Keynote on Concurrency by Raymond Hettinger

• PyBay 2016

  • Being a Core Developer in Python by Raymond Hettinger
  • Python Memory Model & Best Practices by Wesley Chun

SciPy

• SciPy 2020

  • GPU-Accelerated Data Analytics in Python by Joe Eaton
  • Learning from Evolving Data Streams by Jacob Montiel

• SciPy 2019

  • Bayesian Statistics Made Simple by Allen Downey
  • Introduction to Python by Matt Davis
  • Introduction to Numerical Computing with NumPy by Alex Chabot-Leclerc
  • Introduction to Matplotlib by Hannah Aizenman and Thomas Caswell
  • Introduction to Data Processing in Python with Pandas by Daniel Chen
  • Image Analysis in Python with SciPy and Scikit Image by Nunez-Iglesias
  • Turning HPC Systems into Interactive Data Analysis Platforms by A. Banihirwe
  • Modern Time Series Analysis by Aileen Nielsen
  • Visualize any Data Easily, from Notebooks to Dashboards by James Bednar
  • Turn any Notebook into a Deployable Dashboard by James Bednar
  • Productionalizing a Data Science Team by Nicole Carlson
  • Better and Faster Hyper Parameter Optimization with Dask by Scott Sievert
  • Model Remodeling with Modern Deep Learning Frameworks by Ethan Rosenthal
  • Using a Stacking Model Ensemble Approach to Predict Rare Events by Susan Yuhou Xia

• SciPy 2015

EuroPython

• EuroPython, 2019

  • Writing faster Python by Sebastian Witowski
  • Wait, IPython can do that?! by Sebastian Witowski

Meetups

• SF Python

  • Modern Dictionaries by Raymond Hettinger
  • Learning From Games and Puzzles by Raymond Hettinger
  • Docker for Data Science by Aly Sivji

• PyOhio

  • Natural Language Processing in Python by Alice Zhao

Machine Learning and Data Science

• Fun with TPUs by Martin Görner
• TensorFlow, Deep Learning, and Modern Convolutional Neural Nets, Without a PhD by Martin Görner
• Applying Statistical Modeling & Machine Learning to Perform Time-Series Forecasting by Tamara Louie
• Learning scikit learn - An Introduction to Machine Learning in Python by Sebastian Raschka
• Machine Learning with scikit learn by Andreas Mueller
• Episode 1.1: Intro and building a ML framework by Abhishek Thakur
• Episode 1.2: Building an inference for the ML framework by Abhishek Thakur
• Episode 2: A Cross Validation Framework by Abhishek Thakur
• Episode 3: Handling Categorical Features in ML Problems by Abhishek Thakur
• Deploying scikit learn Models in Production by Rajat Arya

Contributing

Please read through our contributing guidelines. Included are directions for opening issues, coding standards, and notes on development.

Creator

• https://github.com/acharles7 If you have any question about this opinionated list, do not hesitate to contact me @charlespatel on Twitter or open an issue on GitHub.

Thanks

Thank you all the speakers at different conferences for teaching us. Happy Learning