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ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

This repo contains the source code in my personal column (https://zhuanlan.zhihu.com/zhaoyeyu), implemented using Python 3.6. Including Natural Language Processing and Computer Vision projects, such as text generation, machine translation, deep convolution GAN and other actual combat code.

An implementation of a complete machine learning solution in Python on a real-world dataset. This project is meant to demonstrate how all the steps of a machine learning pipeline come together to solve a problem!

Python implementation of TextRank algorithm for automatic keyword extraction and summarization using Levenshtein distance as relation between text units. This project is based on the paper "TextRank: Bringing Order into Text" by Rada Mihalcea and Paul Tarau. https://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf

📈This repo contains detailed notes and multiple projects implemented in Python related to AI and Finance. Follow the blog here: https://purvasingh.medium.com

This is a Kia UVO and Hyundai Bluelink written in python. It is primary consumed by home assistant. If you are looking for a home assistant Kia / Hyundai implementation please look here: https://github.com/Hyundai-Kia-Connect/kia_uvo. Much of this base code came from reading bluelinky and contributions to the kia_uvo home assistant project.