Categories
Uncategorized

Python DS Digest: February 2021

A list of popular posts and resources shared on Python DS you might have missed! in January 2021. Topics this month include NLP, data manipulation, machine learning, time series, Jupyter notebooks and dashboards.

NLP

  • datasets • The largest hub of ready-to-use NLP datasets for ML models with fast, easy-to-use and efficient data manipulation tools by Hugging Face.

  • Spacy Course • In the course, you’ll learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches by Ines Montani & SpaCy.

  • ecco • Visualize and explore NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models (like GPT2) by Jay Alammar.

Data Manipulation

Time series

  • Time series Forecasting in Python & R, Part 1 (EDA) • Time series forecasting using various forecasting methods in Python & R in one notebook by Sandeep Pawar.

  • deeptime • Python library for analysis of time series data including dimensionality reduction, clustering, and Markov model estimation by Moritz Hoffmann.

  • adtk • A Python toolkit for rule-based/unsupervised anomaly detection in time series by Arundo Analytics.

Machine Learning Tools

  • giotto-tda • A high-performance topological machine learning toolbox in Python by giotto.ai.

  • automlbenchmark • OpenML AutoML Benchmarking Framework by OpenML.

  • Ludwig • a toolbox that allows to train and test deep learning models without the need to write code, by Ludwig at Uber Open Source.

  • norse • Deep learning with spiking neural networks (SNNs) in PyTorch by Christian Pehle and Jens Egholm .

  • handtracking • Building a Real-time Hand-Detector using Neural Networks (SSD) on Tensorflow by Victor Dibia.

  • Python Autocomplete • Use Transformers and LSTMs to learn Python source code by LabML.

  • AutoGL • An autoML framework & toolkit for machine learning on graphs by the Media and Network Lab at Tsinghua University.

  • shapash • Shapash makes Machine Learning models transparent and understandable by everyone. Developed by MAIF (https://maif.github.io).

  • pyod • A Python Toolbox for Scalable Outlier Detection (Anomaly Detection) by Yue Zhao.

  • uncertainty-toolbox • A python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization by Willie Neiswanger.

Learning Machine Learning

Jupyter

Dashboards and Web Apps

Utilities

Data Science learning resources

  • Coding for Economists Chapter 1. Intro to Mathematics with Code — In this chapter, you’ll learn about doing mathematics with code, including solving equations symbolically by Dr Arthur Turrell.

  • algorithms • Minimal examples of data structures and algorithms in Python by Keon.

  • intro-sc-python • Python Tools for Data Science, Machine Learning, and Scientific computing by Pablo Caceres.

  • Classic Computer Science Problems In Python • Source Code for the Book Classic Computer Science Problems in Python by David Kopec.

  • DS3 Practical Optim for ML • Notebooks from DS3 course on practical optimization by Alexandre Gramfort.

  • Data Science from Scratch (Chapter 8). Gradient Descent: Building gradient descent from the ground up, by Paul Apivat.

Visualisation

Enjoyed this article? Subscribe for future posts by email 👇