Skip to content
/ Machine-Learning- Public

This is my attempt at learning by executing and playing around with data sets to understand ML Algorithms and get a firm grasp on Data Modelling.

License

MIT license
1 star 0 forks Branches Tags Activity
Star
Notifications You must be signed in to change notification settings

AshnaVirdikar/Machine-Learning-

BranchesTags

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

5 Commits
LICENSE
LICENSE
 
 
README.md
README.md
 
 
ann.R
ann.R
 
 
apriori.R
apriori.R
 
 
apriori.py
apriori.py
 
 
artificial_neural_network.py
artificial_neural_network.py
 
 
convolutional_neural_network.py
convolutional_neural_network.py
 
 
decision_tree_classification.R
decision_tree_classification.R
 
 
decision_tree_classification.py
decision_tree_classification.py
 
 
decision_tree_regression.R
decision_tree_regression.R
 
 
decision_tree_regression.py
decision_tree_regression.py
 
 
eclat.R
eclat.R
 
 
grid_search.R
grid_search.R
 
 
grid_search.py
grid_search.py
 
 
hc.R
hc.R
 
 
hierarchical_clustering.py
hierarchical_clustering.py
 
 
k_fold_cross_validation.R
k_fold_cross_validation.R
 
 
k_fold_cross_validation.py
k_fold_cross_validation.py
 
 
k_means_clustering.R
k_means_clustering.R
 
 
k_means_clustering.py
k_means_clustering.py
 
 
k_nearest_neighbors.py
k_nearest_neighbors.py
 
 
kernel_pca.R
kernel_pca.R
 
 
kernel_pca.py
kernel_pca.py
 
 
kernel_svm.R
kernel_svm.R
 
 
kernel_svm.py
kernel_svm.py
 
 
knn.R
knn.R
 
 
lda.R
lda.R
 
 
linear_discriminant_analysis.py
linear_discriminant_analysis.py
 
 
logistic_regression.R
logistic_regression.R
 
 
logistic_regression.py
logistic_regression.py
 
 
multiple_linear_regression.R
multiple_linear_regression.R
 
 
multiple_linear_regression.py
multiple_linear_regression.py
 
 
naive_bayes.R
naive_bayes.R
 
 
naive_bayes.py
naive_bayes.py
 
 
natural_language_processing.R
natural_language_processing.R
 
 
natural_language_processing.py
natural_language_processing.py
 
 
pca.R
pca.R
 
 
polynomial_regression.R
polynomial_regression.R
 
 
polynomial_regression.py
polynomial_regression.py
 
 
principal_component_analysis.py
principal_component_analysis.py
 
 
random_forest_classification.R
random_forest_classification.R
 
 
random_forest_classification.py
random_forest_classification.py
 
 
random_forest_regression.R
random_forest_regression.R
 
 
random_forest_regression.py
random_forest_regression.py
 
 
support_vector_machine.py
support_vector_machine.py
 
 
support_vector_regression.py
support_vector_regression.py
 
 
svm.R
svm.R
 
 
svr.R
svr.R
 
 
thompson_sampling.R
thompson_sampling.R
 
 
thompson_sampling.py
thompson_sampling.py
 
 
upper_confidence_bound.R
upper_confidence_bound.R
 
 
upper_confidence_bound.py
upper_confidence_bound.py
 
 
xg_boost.R
xg_boost.R
 
 
xg_boost.py
xg_boost.py
 
 

Repository files navigation

Machine-Learning-

This is my attempt at learning by executing and playing around with data sets to understand ML Algorithms and get a firm grasp on Data Modelling.

About

This is my attempt at learning by executing and playing around with data sets to understand ML Algorithms and get a firm grasp on Data Modelling.

Resources

Readme

License

MIT license
Activity

Stars

1 star

Watchers

1 watching

Forks

0 forks
Report repository

Releases

No releases published

Packages

No packages published

Languages