GitHub - zhiraslan/machine-learning-projects: The current repository contains ml works that were completed on the course and independently. These include the use of the most popular methods for solving forecasting problems (linear regression, polynomial regression, regression with L1 and L2 regularization), classification (k-nearest neighbor method, decision trees, naive Bayesian classifier, SVM, logistic regression), and clustering (k-means method, DBSCAN). There are also separate notebooks on specific metrics and proprietary implementations of some of the listed algorithms, and dimension reduction methods (PCA, SVM, t-SNE).

Branches Tags

Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
AUC, ROC.ipynb		AUC, ROC.ipynb
BOOSTING.ipynb		BOOSTING.ipynb
Blending.ipynb		Blending.ipynb
Classification - HW1.ipynb		Classification - HW1.ipynb
Classification - HW2.ipynb		Classification - HW2.ipynb
Classification - HW3.ipynb		Classification - HW3.ipynb
Classification algorithms.ipynb		Classification algorithms.ipynb
Clusterization_unneeded.ipynb		Clusterization_unneeded.ipynb
DBSCAN.ipynb		DBSCAN.ipynb
Decision Tree.ipynb		Decision Tree.ipynb
Gradient Descent.ipynb		Gradient Descent.ipynb
K-means (Indian example and implementation).ipynb		K-means (Indian example and implementation).ipynb
KNN HW.ipynb		KNN HW.ipynb
Linear Regression.ipynb		Linear Regression.ipynb
ML3.ipynb		ML3.ipynb
ML_1.ipynb		ML_1.ipynb
Naive Bayes Classifier.ipynb		Naive Bayes Classifier.ipynb
PCA.ipynb		PCA.ipynb
SVD.ipynb		SVD.ipynb
Submission.csv		Submission.csv
Titanic_Kaggle.ipynb		Titanic_Kaggle.ipynb
_Функции_нескольких_переменных,_их_свойства_и_графики_Домашнее.ipynb		_Функции_нескольких_переменных,_их_свойства_и_графики_Домашнее.ipynb
t-SNE.ipynb		t-SNE.ipynb
test.csv		test.csv
textClassification_(NLP).ipynb		textClassification_(NLP).ipynb
titanic.csv		titanic.csv
titanic_test.csv		titanic_test.csv
train.csv		train.csv

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The current repository contains ml works that were completed on the course and independently. These include the use of the most popular methods for solving forecasting problems (linear regression, polynomial regression, regression with L1 and L2 regularization), classification (k-nearest neighbor method, decision trees, naive Bayesian classifier,…

machine-learning-algorithms machine-learning-metrics matplotlib numpy pandas python