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机器学习、深度学习、自然语言处理等人工智能基础知识总结。

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SailVina重构增强版

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机器学习算法python实现

2022-d-

2022年华为杯数学研究生建模D题练手代码，文章

awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

cluster-kmeans-emgmm-pca

Wine Types Clustering using K-Means, EM-GMM and PCA

coursera-ml-andrewng-notes

吴恩达老师的机器学习课程个人笔记

deepgs

DeepGS: Deep Representation Learning of Graphs and Sequences for Drug-Target Binding Affinity Prediction (ECAI 2020)

demo-graphdta-

小白入门DTA方向，GraphDTA作为经典的DTA模型，对于python基础不好，深度学习代码实操不强者，本github将代码细致的进行注释阐述，旨在记录学习过程，帮助更多入门者尽快入门！

ensemble-learning

代码实操

huaweicupmathmodel

2021年华为杯第十八届**研究生数学建模竞赛D题解决方案（国二）

machine_learning

周志华《机器学习》阅读笔记

pocketdta

PocketDTA

predict-wine-type-quality

Data Set Information: The dataset was downloaded from the UCI Machine Learning Repository. The two datasets are related to red and white variants of the Portuguese "Vinho Verde" wine. The reference [Cortez et al., 2009]. Due to privacy and logistic issues, only physicochemical (inputs) and sensory (the output) variables are available (e.g. there is no data about grape types, wine brand, wine selling price, etc.). These datasets can be viewed as classification or regression tasks. The classes are ordered and not balanced (e.g. there are munch more normal wines than excellent or poor ones). Outlier detection algorithms could be used to detect the few excellent or poor wines. Also, we are not sure if all input variables are relevant. So it could be interesting to test feature selection methods. Two datasets were combined and few values were randomly removed. Attribute Information: For more information, read [Cortez et al., 2009]. Input variables (based on physicochemical tests): 1 - fixed acidity 2 - volatile acidity 3 - citric acid 4 - residual sugar 5 - chlorides 6 - free sulfur dioxide 7 - total sulfur dioxide 8 - density 9 - pH 10 - sulphates 11 - alcohol Output variable (based on sensory data): 12 - quality (score between 0 and 10) Acknowledgements: P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009.

red-wine-quality-accuracy-0.9175-

The Red Wine Quality dataset from kaggle. Data is provided of the composition of the wine having different chemicals. I have used pandas to manipulate the data and seaborn to visualize the data. Finally I have made predictions on the wine quality by using various models from the scikit-learn.

wine-quality-predictions

Predicting the Quality of Red Wine using Machine Learning Algorithms for Regression Analysis, Data Visualizations and Data Analysis.

wine-type-classification

Predicted the type of wine(Red/White) by analyzing a data set of chemical properties and evaluations by wine experts, running classification models and regression models in Python to predict better quality wine.

wine_type-prediction

对葡萄酒类型的几种简单的模型预测