đ Python Data Science Digest July 2021
A thematic list of Python posts, packages and tools that were popular in the last month
A list of the most popular posts featured on Python Posts you might have missed! in June 2021. All of the most exciting Python resources in deep learning, machine learning, visualisation and data analysis
Featured posts
đ¤ A standard framework for modelling Deep Learning Models for tabular data by Manu Joseph.
Deep learning comes to tabular data? Deep learning has famously had huge successes in tasks involving unstructured data like images, video and audio. However for tabular data, deep learning approaches often perform less well than tree-based learners like boosted tree ensembles.Â
pytorch_tabular
 provides leading-edge implementations of neural networks for tabular data in user-friendly wrappers.
đ ď¸ awesome-mlops ⢠A curated list of references for MLOps by Larysa Visengeriyeva.
Learn MLOps with this comprehensive list of materials! MLOps is âa set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently." (Wikipedia). Although this is a critical capability for anyone using and deploying models, the technology behind these practises are relatively new and can be overwhelming to new data scientists.
đ lux ⢠Python API for Intelligent Visual Data Discovery by Lux.
Boost your EDA! We all know exploring data is time consuming! Lux provides visualisation tools for exploring pandas dataframes interactively and automatically via jupyter notebook widgets. For a quick primer of the main functionality, check out this lightening talk.
Learning materials
Introduction â Scientific computing with Python by Agah Karakuzu.
ML-foundations ⢠Algebra, Calculus, Statistics: Computer Science by Jon Krohn.
pycaret
 ⢠An open-source, low-code machine learning library in Python by PyCaret.scientific-visualization-book ⢠An open access book on scientific visualization using python and matplotlib (ď¸WIP) by Nicolas P. Rougier.
Data Science Cheatsheet by Maverick Lin.
Visualisation
shifterator
 ⢠Interpretable data visualizations for understanding how texts differ at the word level by Ryan J. Gallagher.ipygany
: Jupyter into the third dimension ⢠Scientific visualization in the Jupyter notebook by Martin Renou Project Jupyter.How to visualize decision trees by Terence Parr and Prince Grover.
altair
 ⢠Declarative statistical visualization library for Python by Brian E. Granger and Jake Vanderplas.A high-level app and dashboarding solution for Python by Panel.
Create a Model Card with Scikit-Learn by Google Cloud.
PtitPrince
 ⢠python version of raincloud by Davide Poggialipython-ternary
 ⢠Ternary plotting library for python with matplotlib by Marc Harper.dtale
 ⢠Visualizer for pandas data structures by Man Group.How to use Seaborn Data Visualization for Machine Learning by Machine Learning Mastery.
Bar Chart Race ⢠Create animated bar chart races in Python with matplotlib by Ted Petrou.
Python Graph Gallery by Yan Holtz.
Machine learning: models
Multivariate Anomaly Detection using Isolation Forests in Python by Florian Mßller.
How to use Scikit learn in a Machine Learning Project for Beginner? (Sklearn Tutorial)Â by Malick A. Sarr.
Machine-Learning ⢠Implementation of different ML Algorithms from scratch, written in Python 3.x by Gautam J.
Mlxtend
 (machine learning extensions) is a Python library of useful tools for the day-to-day data science tasks by Sebastian Raschka.
Machine learning: feature selection
feature_engine
 ⢠Feature engineering package with sklearn like functionality by Soledad Galli.Framework for Data Preparation Techniques in Machine Learning by Machine Learning Mastery.
Feature Selection and EDA in Machine Learning by Visual Design.
Machine learning: MLOps & deployment
client
 ⢠A tool for visualizing and tracking your machine learning experiments by Weights and Biases.opyrator
 ⢠Turns your machine learning code into microservices with web API, interactive GUI, and more by Machine Learning Tooling.Model Monitoring Enables Robust Machine Learning Applications by Ben Lorica and Paco Nathan.
Operationalizing Machine Learning models with Azure ML and Power BI ⢠Sandeep Pawar by Sandeep Pawar.
Deep learning
Image classification with modern MLP models by Khalid Salama.
Video Classification with Transformers by Sayak Paul.
Introduction to Autoencoders ¡ Deep Learning by Alfredo Canziani.
For Researchers ⢠Explore and extend models from the latest cutting edge research by PyTorch.
Statistics
tslearn
 ⢠A machine learning toolkit dedicated to time-series data by Romain Tavenard & tslearn contributors.Causal inference 4: Causal Diagrams, Markov Factorization, Structural Equation Models by Ferenc Huszår and Patrik Reizinger.
Meta Learners â Causal Inference for the Brave and True by Matheus Facure Alves.
An intuitive, visual guide to copulas by Thomas Wiecki.
Topic Modelling in Python by Coding Club.
Spatial methods
End-to-End Google Earth Engine (Full Course Material)Â by Ujaval Gandhi.
GeostatsPy
 Intro Course ⢠Introduction to spatial data analytics and machine learning with GeostatsPy Python package by Michael Pyrcz.satpy
 ⢠Python package for earth-observing satellite data processing by Pytroll.meteostat-python ⢠Access and analyze historical weather and climate data with Python by Meteostat.
Tools and Utilities
Why we reluctantly work with regex - Because, sometimes thereâs no choice by Randy Au.
traingenerator
 ⢠A web app to generate template code for machine learning by Johannes Rieke.Setting up VS Code for Python Development like RStudio ⢠Steven M. Mortimer by Steven M. Mortimer.
Thirty-two Python Tools and Package Libraries to Increase your Machine Learning Productivity by Bruce H. Cottman.
How to Use Jupyter Notebook in 2020: A Beginnerâs Tutorial by Dataquest.
đThanks for reading!
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