Machine-Learning-

Most of machine learning algorithms will be available here. (Both in Python and R)

I will be adding machine learning and deep learning algorithms one by one. Follow me and keep yourself updated !! Most of these algorithms were part of machine learning a-z course on udemy. I am using github as a playing field.

currently available implementations

1. Eclat
2. Apriori
3. simple linear regression
4. polynomial regression
5. Decision tree regression
6. Random forest regression
7. Logistic regression
8. KNN classification
9. SVM classiffier
10. Kernel SVM classifier
11. Naive bayes classifier
12. Decision Tree classification
13. Random Forest Classification
14. Natural language processing - Basic implementation of Bag of Words
15. Principle component analysis
16. artificial neural network - example_01
17. .................

Solving one machine learning problem at a time !

1. Wine problem - http://archive.ics.uci.edu/ml/datasets/Wine+Quality I have considered white wine dataset for classification task. Please find more details in the above link.
2. Cat or Dog - Image recognistion using Deep Convolutional Neural Network. I have used Keras with tensorflow backend to train CNN model.
3. Codekicker (aaron.ai task) - Text classification / clustering task I have used Random Forest Classifier / K-Means clustering
4. Sick or Healthy (Luminovo challenge) - Artificial neural network iplmentation of classification Task is to classify whether person is healthy or sick. Deep learning implementaion.