Use Python and NLTK to build out your own text classifiers and solve common NLP problems
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Jan 15, 2020 - Jupyter Notebook
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Use Python and NLTK to build out your own text classifiers and solve common NLP problems
python nlp api natural-language-processing unsupervised linear-regression scikit-learn markov-chain pandas lda supervised latent-dirichlet-allocation tokenization binary-classifier
The uploaded codes help to classify emails into spam and non spam classes by using Support Vector Machine classifier.
machine-learning-algorithms support-vector-machines lemmatization email-spam-filter stopwords-removal binary-classifier
Build intelligent applications that can interpret the human language to deliver impactful results
python nlp api natural-language-processing linear-regression scikit-learn markov-chain pandas supervised-learning lda unsupervised-learning latent-dirichlet-allocation tokenization binary-classifier
Silicon Valley inspired binary classifier to identify hot-dogs and not-hot-dogs. Source: https://www.youtube.com/watch?v=vIci3C4JkL0
Access the Linear or RBF kernel SVM from OCaml using the R e1071 or svmpath packages
An image classifier that determines whether an image contains a dog or a cat.
Diabetes Prediction using Three Machine Learning Algorithms - Logistic Regression, Random Forest & SVM
python machine-learning random-forest svm ml python3 pip logistic-regresion pipenv mrmr binary-classifier
Short project done for the course "Introduction to Machine Learning" @ UniTS.
This repository contains some machine learning projects as a practise on machine learning course on Coursera for Prof. Andrew Ng from Stanford University.
machine-learning matlab machine-learning-algorithms octave classification logistic-regression machinelearning machine-learning-coursera logistic-regresion classification-algorithm logistic-regression-algorithm machine-learning-practice classification-model binary-classifiers logisticregression machine-learning-projects binary-classifications binary-classifier binary-classificaiton machine-learning-matlab
In this we trained a model to detect if there is a tumor in the brain image given to the model. Meaning a model for binary class with an accuracy of above 90 for same and cross validation.
python binary tensorflow keras pandas kaggle classification brain binary-classification nueral-networks classification-algorithm classification-model tumor-detection binary-classifier binary-classificaiton binaryclassification tumor-classification brain-tumor-classification brain-tumor-detection
The primary objective of this project was to develop a predictive model capable of accurately determining whether a bank customer will maintain or close their account. Kaggle Playground Series S4E1
data-science machine-learning ai neural-network kaggle kaggle-competition ensemble ensemble-model binary-classification binary-classifier bank-churning-model
A novel ML-based binary classifier to tell viral and non-viral long reads apart in metagenomic samples.
machine-learning virus genomics metagenomics dna healthcare microbiology biodiversity oxford-nanopore virome voting-classifier binary-classifier long-read-sequencing viromics
Pipeline for mask detection over a Pi camera video feed.
python raspberry-pi iot tensorflow python3 dataset face-detection binary-classification iot-device iot-application coral binary-classifier tflite edge-tpu tflite-models mask-detection
Binary Linear Classifier - AI Supervised Algorithm
machine-learning supervised-learning classification-algorithm perceptron-learning-algorithm linear-classifier binary-classifier
A binary classifier to test whether an image belongs to the "hot dog" class or the "not hot dog" class, as seen on HBO's Silicon Valley.
deep-learning tensorflow hbo vgg16 silicon-valley inception-v3 resnet-50 mobilenet-v2 binary-classifier hot-dog not-hot-dog
Explores classification methods to predict whether a student will answer a test question correctly
Binary classifier that determines whether an image is of a cat or a dog.
Binary classification model using PyTorch to predict heart failure using clinical data.
Using machine learning and neural networks, use the features in the provided dataset to create a binary classifier that can predict whether applicants will be successful if funded by Alphabet Soup.
deep-neural-networks deep-learning image-processing regression mnist mnist-classification resnet regression-models cifar10 computervision facial-expression-recognition regression-algorithms cifar-10 mnist-handwriting-recognition binary-classifier cat-vs-dog digit-recognition-mnist protein-classification wine-quality-prediction
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