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arnaldog12 / Machine_Learning

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Estudo e implementação dos principais algoritmos de Machine Learning em Jupyter Notebooks.
python machine-learning neural-network naive-bayes linear-regression machine-learning-algorithms regression feature-selection logistic-regression kmeans adaboost decision-trees polynomial-regression knn principal-component-analysis redes-neurais-artificiais linear-discriminant-analysis multilinear-regression
  • Updated Apr 11, 2021
  • Jupyter Notebook

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

MBKraus / Predicting_real_estate_prices_using_scikit-learn

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Predicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression (via scikit-learn)
real-estate python machine-learning neural-network random-forest lasso xgboost polynomial ensemble-learning ols decision-trees ridge-regression polynomial-regression knn multi-layer-perceptron amsterdam predicting-housing-prices lasso-regression mlp-regressor knn-regression
  • Updated Apr 9, 2019
  • Python

agrawal-priyank / machine-learning-regression

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Built house price prediction model using linear regression and k nearest neighbors and used machine learning techniques like ridge, lasso, and gradient descent for optimization in Python
python machine-learning linear-regression coursera gradient-descent ridge-regression polynomial-regression university-of-washington house-price-prediction multiple-regression machine-learning-regression
  • Updated Jan 20, 2018
  • Jupyter Notebook

eaglewarrior / Machine-learning

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Implement different ML algorithms with open source data files

eaglewarrior
eaglewarrior commented Oct 4, 2021
  1. Star it
  2. Fork the repo
  3. Clone it onto your PC.
  4. Create a folder with your GitHub username
  5. Create separate files for all the issues you are solving and always open an issue which has all details of the process or method you will use to perform anomaly detection and wait till it is assigned (not more than 2-3 hours it will take, we are passionate open source developers )
  6. Open PRs
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good first issue Hacktoberfest

anishsingh20 / Statistical-Learning-using-R

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This is a Statistical Learning application which will consist of various Machine Learning algorithms and their implementation in R done by me and their in depth interpretation.Documents and reports related to the below mentioned techniques can be found on my Rpubs profile.
r machine-learning-algorithms statistical-learning datascience data-analysis logistic-regression regularization decision-trees predictive-modeling polynomial-regression clustering-algorithm svm-classifier k-nn boosting generalized-additive-models supervised-machine-learning bagging depth-interpretation discriminant-anlaysis
  • Updated Apr 7, 2018
  • R
mida

srohit0 / mida

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Selected problems and their solutions from the book on "Machine Intelligence in Design Automation"
machine-learning deep-neural-networks deep-learning neural-network machine-learning-algorithms artificial-intelligence artificial-neural-networks logistic-regression feedforward-neural-network polynomial-regression vlsi machine-intelligence design-automation electronic-design-automation vlsi-circuits vlsi-design vlsi-machine-learning vlsi-deep-learning
  • Updated Dec 9, 2018
  • Jupyter Notebook
synthetic_well-log_polynomial_regression

abhishekdbihani / synthetic_well-log_polynomial_regression

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This project attempts to construct a missing well log from other available well logs, more specifically an NMR well log from the measured Gamma Ray (GR), Caliper, Resistivity logs and the interpreted porosity from a well.
feature-selection nmr feature-engineering polynomial-regression petroleum petrophysics well-logs porosity nmr-log walker-ridge keathley-canyon resistivity-log
  • Updated Sep 21, 2020
  • Jupyter Notebook

S-B-Iqbal / Predicting-Power-Output-of-a-combined-cycle-power-plant.

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The objective of the Project is to predict ‘Full Load Electrical Power Output’ of a Base load operated combined cycle power plant using Polynomial Multiple Regression. Concepts : 1) Clustering, 2) Polynomial Regression, 3) LASSO, 4) Cross-Validation, 5) Bootstrapping
cross-validation lasso silhouette polynomial-regression bootstrapping-statistics k-means-clustering
  • Updated Sep 22, 2018
  • Jupyter Notebook

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