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kk7nc / Text_Classification

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Text Classification Algorithms: A Survey
deep-learning random-forest text-classification recurrent-neural-networks naive-bayes-classifier dimensionality-reduction logistic-regression document-classification convolutional-neural-networks text-processing decision-trees boosting-algorithms support-vector-machines hierarchical-attention-networks nlp-machine-learning conditional-random-fields k-nearest-neighbours deep-belief-network rocchio-algorithm deep-neural-network
  • Updated Mar 9, 2022
  • Python

Skylark0924 / Machine-Learning-is-ALL-You-Need

Star 272

🔥🌟《Machine Learning 格物志》: ML + DL + RL basic codes and notes by sklearn, PyTorch, TensorFlow, Keras & the most important, from scratch!💪 This repository is ALL You Need!
qlearning random-forest tensorflow keras deep-reinforcement-learning pytorch lstm gan dqn naive-bayes-classifier logistic-regression resnet convolutional-neural-networks support-vector-machine decision-trees ddpg k-nearest-neighbours actor-critic trpo ppo
  • Updated Apr 24, 2022
  • Python

jayshah19949596 / Machine-Learning-Models

Star 221

Decision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means
neural-network random-forest linear-regression machine-learning-algorithms naive-bayes-classifier supervised-learning gaussian-mixture-models logistic-regression kmeans decision-trees knn principal-component-analysis dynamic-time-warping kmeans-clustering em-algorithm kmeans-algorithm singular-value-decomposition knn-classification gaussian-classifier value-iteration-algorithm
  • Updated May 15, 2017
  • MATLAB

srafay / Machine_Learning_A-Z

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Learning to create Machine Learning Algorithms
machine-learning-algorithms datascience naive-bayes-classifier logistic-regression support-vector-machine polynomial-regression decision-tree-classifier dataprocessing classification-algorithims random-forest-regressor support-vector-regression multiple-regression decision-tree-regression k-means-implementation-in-python k-means-clustering kernel-svm random-forest-classifier simple-linear-regression k-nearest-neighbors machine-learning-az
  • Updated Jun 15, 2021
  • Python

milaan9 / Machine_Learning_Algorithms_from_Scratch

Star 162

This repository explores the variety of techniques and algorithms commonly used in machine learning and the implementation in MATLAB and PYTHON.
data-science machine-learning random-forest machine-learning-algorithms naive-bayes-classifier decision-trees fitting-algorithm dynamic-time-warping machine-learning-python singular-value-decomposition value-iteration-algorithm frequentist-methods error-functions gaussian-naive-bayes machine-learning-matlab python4datascience tutor-milaan9 matlab4datascience
  • Updated Jun 12, 2022
  • Jupyter Notebook

pavankalyan1997 / Machine-learning-without-any-libraries

Star 128

This is a collection of some of the important machine learning algorithms which are implemented with out using any libraries. Libraries such as numpy and pandas are used to improve computational complexity of algorithms
machine-learning-algorithms jupyter-notebook naive-bayes-classifier artificial-neural-networks k-means-clustering simple-linear-regression knn-classifier multiple-linear-regression
  • Updated Feb 5, 2019
  • Jupyter Notebook
MLWithPytorch

Mayurji / MLWithPytorch

Star 116

Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch
machine-learning linear-regression machine-learning-algorithms python3 pytorch naive-bayes-classifier pca-analysis gaussian-mixture-models logistic-regression decision-trees ridge-regression naive-bayes-algorithm kmeans-clustering svm-classifier lasso-regression knn-classification pytorch-implementation tfidf-vectorizer adaboost-algorithm
  • Updated Dec 15, 2021
  • Python

gandalf1819 / Stock-Market-Sentiment-Analysis

Star 74

Identification of trends in the stock prices of a company by performing fundamental analysis of the company. News articles were provided as training data-sets to the model which classified the articles as positive or neutral. Sentiment score was computed by calculating the difference between positive and negative words present in the news article. Comparisons were made between the actual stock prices and the sentiment scores. Naive Bayes, OneR and Random Forest algorithms were used to observe the results of the model using Weka
machine-learning r random-forest stock-market naive-bayes-classifier news-articles classification-algorithm sentiment-scores fundamental-analysis techincal-analysis
  • Updated Nov 10, 2020
  • R

farhanchoudhary / Machine_Learning_A-Z_All_Codes_and_Templates

Star 30

All codes, both created and optimized for best results from the SuperDataScience Course
natural-language-processing reinforcement-learning deep-learning clustering cross-validation naive-bayes-classifier thompson-sampling neural-networks classification dimensionality-reduction grid-search principal-component-analysis clustering-algorithm upper-confidence-bounds k-fold xgboost-algorithm association-rule-learning machine-learning-az
  • Updated Nov 5, 2017
  • Python

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