scikit-learn: machine learning in Python

Investigation for PyDataLondon 2023 and ODSC 2023 conference comparing Pandas 2, Polars and Dask

matplotlib: plotting with Python

A library to find and visualise the most interesting slices in multidimensional data

The uncompromising Python code formatter

Examples and guides for using the OpenAI API

Python数据挖掘教程

100 data puzzles for pandas, ranging from short and simple to super tricky (60% complete)

100 numpy exercises (with solutions)

Code Repository for The Kaggle Book, Published by Packt Publishing

基于机器学习方法构建多因子选股模型：RandomForest, GBDT, Adaboots, xgboost，MLP, Linear Model, LSTM

1st Place Solution for O2O Coupon Usage Forecast

Interview = 简历指南 + 算法题 + 八股文 + 源码分析

Getting start with PySpark and MLlib

Apache Spark - A unified analytics engine for large-scale data processing

A handbook for ML built on answers.

Bias Auditing & Fair ML Toolkit

AI and Machine Learning with Kubeflow, Amazon EKS, and SageMaker

Repository for a workshop on Bayesian Decision Analysis

Workshop on Bayesian inference using PyMC

Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

Lifetime value in Python

Python code for common Machine Learning Algorithms

List of Data Science Cheatsheets to rule the world

最全数据分析资料汇总（含python、爬虫、数据库、大数据、tableau、统计学等）

My solution to the book <A collection of Data Science Take-home Challenges>

此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。

算法理论基础知识应知应会

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.