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Limdu is a machine-learning framework for Node.js. It supports multi-label classification, online learning, and real-time classification. Therefore, it is especially suited for natural language understanding in dialog systems and chat-bots.Limdu is in an "alpha" state - some parts are working (see this readme), but some parts are missing or not tested. Contributions are welcome.

classifier classification categorization text-classification natural-lanaguage-understanding machine-learning multi-label multilabel multi-class multiclass online-learning naive-bayes winnow perceptron svm linear-svm binary-relevance one-vs-allCourse materials for General Assembly's Data Science course in Washington, DC (8/18/15 - 10/29/15).

data-science machine-learning scikit-learn data-analysis pandas jupyter-notebook course linear-regression logistic-regression model-evaluation naive-bayes natural-language-processing decision-trees ensemble-learning clustering regular-expressions web-scraping data-visualization data-cleaningPractice and tutorial-style notebooks covering wide variety of machine learning techniques

numpy statistics pandas matplotlib regression scikit-learn classification principal-component-analysis clustering decision-trees random-forest dimensionality-reduction neural-network deep-learning artificial-intelligence data-science machine-learning k-nearest-neighbours naive-bayesI just built out v2 of this project that now gives you analytics info from your models, and is production-ready. machineJS is an amazing research project that clearly proved there's a hunger for automated machine learning. auto_ml tackles this exact same goal, but with more features, cleaner code, and the ability to be copy/pasted into production.

machine-learning data-science machine-learning-library machine-learning-algorithms ml data-scientists javascript-library scikit-learn kaggle numerai automated-machine-learning automl auto-ml neuralnet neural-network algorithms random-forest svm naive-bayes bagging optimization brainjs date-night sklearn ensemble data-formatting js xgboost scikit-neuralnetwork knn k-nearest-neighbors gridsearch gridsearchcv grid-search randomizedsearchcv preprocessing data-formatter kaggle-competitionRuby scoring API for Predictive Model Markup Language (PMML).Currently supports Decision Tree, Random Forest Naive Bayes and Gradient Boosted Models.

ruby-gem pmml random-forest classification rubyml machine-learning gradient-boosting-classifier gbm gradient-boosted-models decision-tree naive-bayesInspired by Sentan node-sentiment. This gem can be used separately or integrated with rails app.

rails machine-learning sentiment-analysis naive-bayesSimple Gaussian Naive Bayes classifier implementation. It also implements 5-fold cross-validation. Compared performance with Zero-R algorithm.

machine-learning ml naive-bayes-classifier gaussian naive-bayes naive-bayes-algorithmsux0r is a blogging package, an RSS aggregator, a bookmark repository, and a photo publishing platform with a focus on Naive Bayesian categorization and probabilistic content. OpenID enabled (version 1.1); as both a consumer and a provider. Current status: maintanence.

naive-bayes legacy cms openid rss-aggregator abandoned blog bookmarks-manager photo-gallery bayes-algorithmEsse repositório foi criado com a intenção de difundir o ensino de Machine Learning em português. Os algoritmos aqui implementados não são otimizados e foram implementados visando o fácil entendimento. Portanto, não devem ser utilizados para fins de pesquisa ou outros fins além dos especificados.

machine-learning machine-learning-algorithms adaboost decision-trees kmeans knn linear-discriminant-analysis principal-component-analysis naive-bayes regression linear-regression neural-network redes-neurais-artificiais multilinear-regression polynomial-regression feature-selectionA naive bayes text classifier. There are two methods of classification: io.Reader or string. To classify strings, use the TrainString or ClassifyString functions. To classify larger sources, use the Train and Classify functions that take an io.Reader as input.

machine-learning naive-bayes classificationWhichX is a Naive Bayes' Classifier written in Javascript for classifying short text descriptions into categories. It is a very small library with a very simple API and no dependencies. To see a working demo you can also go to http://www.rudikershaw.com/articles/whichpet. If you are using Node start by requiring whichx.

nodejs classifier machine-learning natural-language-processing library text-classification naive-bayes naive-bayes-classifier bayesian bayes bayes-classifier naive-bayes-classification natural-language-understanding node-ml text-classifier naive-bayes-text-classifier naive-bayes-text-classification naive text nlp natural-languageIn this project, over a series of blog posts I'll be buidling a model document classification, also known as text classification and deploying the model as part of a web application to predict the topic of research papers from their abstract. In the first blog post I will be working with the Scikit-learn library and an imbalanced dataset (corpus) that I will create from summaries of papers published on arxiv. The topic of each paper is already labeled as the category therefore alleviating the need for me to label the dataset. The imbalance in the dataset will be caused by the imbalance in the number of samples in each of the categories we are trying to predict. Imbalanced data occurs quite frequently in classification problems and makes developing a good model more challenging. Often times it is too expensive or not possible to get more data on the classes that have to few samples. Developing strategies for dealing with imbalanced data is therefore paramount for creating a good classification model. I will cover some of the basics of dealing with imbalanced data using the Imbalance-Learn library as well as building a Naive Bayes classifier and Support Vector Machine using from Scikit-learn. I will also over the basics of term frequency-inverse document frequency and visualizing it using the Plotly library.

nlp docker data-science machine-learning natural-language-processing text-classification naive-bayes scikit-learn nltk document-classification support-vector-machine imbalanced-data imbalanced-learning fastapi
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