Top #data-science Tools & Software

scikit-learn

github

scikit-learn: machine learning in Python

keras

github

Deep Learning for humans

30-Days-Of-Python

github

The 30 Days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than 100 days. Follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw

pandas

github

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

airflow

github

Apache Airflow - A platform to programmatically author, schedule, and monitor workflows

streamlit

github

Streamlit — A faster way to build and share data apps.

gradio

github

Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!

ray

github

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

spaCy

github

💫 Industrial-strength Natural Language Processing (NLP) in Python

ML-From-Scratch

github

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

pytorch-lightning

github

Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.

data-science-ipython-notebooks

github

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

d2l-en

github

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

prefect

github

Prefect is a workflow orchestration framework for building resilient data pipelines in Python.

awesome-mlops

github

A curated list of references for MLOps

RD-Agent

github

Research and development (R&D) is crucial for the enhancement of industrial productivity, especially in the AI era, where the core aspects of R&D are mainly focused on data and models. We are committed to automating these high-value generic R&D processes through R&D-Agent, which lets AI drive data-driven AI. 🔗https://aka.ms/RD-Agent-Tech-Report

tpot

github

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.