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15 search results found

Machine Learning With Python ⭐ 2,980

Practice and tutorial-style notebooks covering wide variety of machine learning techniques

Ruby Scoring API for PMML

Text Classification Cn ⭐ 33

中文文本分类实践，基于搜狗新闻语料库，采用传统机器学习方法以及预训练模型等方法

Classifier ⭐ 31

A general purpose text classifier

20 Newsgroups_text Classification ⭐ 24

"20 newsgroups" dataset - Text Classification using Multinomial Naive Bayes in Python.

Text Classification ⭐ 23

Text Classification using Multinomial Naive Bayes (implemented from scratch in python3)

Ml Spam Sms Classification ⭐ 23

Naive Bayesian, SVM, Random Forest Classifier, and Deeplearing (LSTM) on top of Keras and wod2vec TF-IDF were used respectively in SMS classification

Naive Bayes ⭐ 18

A Python implementation of Naive Bayes from scratch.

Simple Implementation Of Ml Algorithms ⭐ 17

My simplest implementations of common ML algorithms

Text Classification Benchmark ⭐ 16

文本分类基准测试

Textclassificationapp ⭐ 15

Building and Deploying A Serverless Text Classification Web App

Deeplearning.ai Natural Language Processing ⭐ 12

My solutions to the assignments in the NLP Specialization offered by DeepLearning.AI on Coursera.

Emoji Prediction Using Deep Learning ⭐ 7

This project aims to understand the underlying semantics of the text sentence using natural processing techniques to predict reasonable emojis based on the context.

Unsupervised Features Learning For Image Classification ⭐ 6

Recently, image classification draw attentions of many researchers. The need of object recognition grows drastically, especially in the context of biometric, biomedical imaging and real time scene understanding. Computer vision task is the most challenging in machine learning. For that reason, it's fundamental to tackle this concern using appropriate clustering and classification techniques. However, the quest for the best unsupervised features extraction remain an open problem even if CNNs r

Machine Learning Practice ⭐ 5

Repository containing introduction to scikit-learn to provide hands-on problem solving experience for all the methods and models learnt in MLT.

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