Text Classification Python
An example of retails products classification using scikit and nltk -
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Practical Machine Learning With Python | 2,054 | a year ago | 19 | apache-2.0 | Jupyter Notebook | |||||
| Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system. | ||||||||||
| Speech_signal_processing_and_classification | 203 | a year ago | 3 | mit | Python | |||||
| Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8]. | ||||||||||
| Awesome Text Classification | 144 | 6 years ago | apache-2.0 | |||||||
| Awesome-Text-Classification Projects,Papers,Tutorial . | ||||||||||
| Usent | 82 | 3 years ago | other | Python | ||||||
| Subjectivity and sentiment classification using polarity lexicons | ||||||||||
| Nlpbot | 82 | 6 years ago | gpl-3.0 | Python | ||||||
| Simple ChatBot introducing NLP and Machine Learning for Classification of Sentences | ||||||||||
| Textfool | 79 | 5 years ago | 4 | mit | Python | |||||
| Plausible looking adversarial examples for text classification | ||||||||||
| Nlp_workshop_odsc_europe20 | 36 | 4 years ago | gpl-3.0 | Jupyter Notebook | ||||||
| Extensive tutorials for the Advanced NLP Workshop in Open Data Science Conference Europe 2020. We will leverage machine learning, deep learning and deep transfer learning to learn and solve popular tasks using NLP including NER, Classification, Recommendation \ Information Retrieval, Summarization, Classification, Language Translation, Q&A and Topic Models. | ||||||||||
| Collective.classification | 25 | 12 years ago | 5 | August 05, 2010 | 1 | Python | ||||
| Content classification/clustering through language processing | ||||||||||
| Text Classification Python | 17 | 5 years ago | 1 | Python | ||||||
| An example of retails products classification using scikit and nltk - | ||||||||||
| Textclassificationapp | 15 | a year ago | 4 | mit | Jupyter Notebook | |||||
| Building and Deploying A Serverless Text Classification Web App | ||||||||||
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