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In This repository I made some simple to complex methods in machine learning. Here I try to build template style code.

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MIT license
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Nikronic/Machine-Learning-Models

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Part 1 - Data Preprocessing

Part 1 - Data Preprocessing

 
 

Part 10 - Model Selection & Boosting

Part 10 - Model Selection & Boosting

 
 

Part 2 - Regression

Part 2 - Regression

 
 

Part 3 - Classification

Part 3 - Classification

 
 

Part 4 - Clustering

Part 4 - Clustering

 
 

Part 5 - Association Rule Learning

Part 5 - Association Rule Learning

 
 

Part 6 - Reinforcement Learning

Part 6 - Reinforcement Learning

 
 

Part 7 - Natural Language Processing/Section 20 - Natural Language Processing

Part 7 - Natural Language Processing/Section 20 - Natural Language Processing

 
 

Part 8 - Deep Learning

Part 8 - Deep Learning

 
 

Part 9 - Dimensionality Reduction

Part 9 - Dimensionality Reduction

 
 

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Tools

 
 

.gitignore

.gitignore

 
 

LICENSE

LICENSE

 
 

README.md

README.md

 
 

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Machine-Learning-Models

In This repository I made some simple to complex methods in machine learning. Here I try to build template style code. All of these models will be used on real datasets in another Jupyter notebook file with full documentation.

Goal

I used more general form and some other methods for building templates to import them into a Jupyter notebook.

Resource

Superdatascience.com

Citation

Please cite this project as:

Nikan Doosti. (2020). Nikronic/Machine-Learning-Models: DOI Release (v0.1-alpha). Zenodo. https://doi.org/10.5281/zenodo.3838653

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In This repository I made some simple to complex methods in machine learning. Here I try to build template style code.

Topics

reinforcement-learning random-forest svm naive-bayes linear-regression cnn thompson-sampling xgboost pca logistic-regression apriori lda ann decision-tree nlp-machine-learning k-nn support-vector-regression kernel-pca eclat upper-confidence-bound

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Readme

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MIT license
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May 21, 2020

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