2019 Sohu Contest
2019年4月8日，第三届搜狐校园内容识别算法大赛。
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Text_classification | 1,621 | a year ago | 1 | mit | Python | |||||
| Text Classification Algorithms: A Survey | ||||||||||
| Dat8 | 1,549 | 2 years ago | Jupyter Notebook | |||||||
| General Assembly's 2015 Data Science course in Washington, DC | ||||||||||
| Ml_code | 94 | 2 years ago | 2 | apache-2.0 | Python | |||||
| A repository for recording the machine learning code | ||||||||||
| Grasp | 66 | 4 months ago | 5 | bsd-3-clause | Python | |||||
| Essential NLP & ML, short & fast pure Python code | ||||||||||
| Machine Learning Models | 26 | 2 years ago | 2 | mit | Python | |||||
| In This repository I made some simple to complex methods in machine learning. Here I try to build template style code. | ||||||||||
| 2019 Sohu Contest | 19 | 5 years ago | 1 | Python | ||||||
| 2019年4月8日，第三届搜狐校园内容识别算法大赛。 | ||||||||||
| Supervised Machine Learning | 15 | 4 years ago | Jupyter Notebook | |||||||
| This repo contains regression and classification projects. Examples: development of predictive models for comments on social media websites; building classifiers to predict outcomes in sports competitions; churn analysis; prediction of clicks on online ads; analysis of the opioids crisis and an analysis of retail store expansion strategies using Lasso and Ridge regressions. | ||||||||||
| Machine Learning Using Python | 15 | a year ago | Python | |||||||
| Regression, Classification and Clustering | ||||||||||
| Voice4rural | 12 | 2 years ago | Python | |||||||
| A complete one stop solution for all the problems of Rural area people. 👩🏻🌾 | ||||||||||
| Sentimentanalysis Python Demo | 10 | 2 years ago | unlicense | Jupyter Notebook | ||||||
| Submission of an in-class NLP sentiment analysis competition held at Microsoft AI Singapore group. This submission entry explores the performance of both lexicon & machine-learning based models | ||||||||||
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