Machine-Learning-A-Z

This is a repository for the Udemy course on Machine Learning with Python and R.

Name		Name	Last commit message	Last commit date
Latest commit History 34 Commits
Ads_CTR_Optimisation.csv		Ads_CTR_Optimisation.csv
Apriori Python Lecture.ipynb		Apriori Python Lecture.ipynb
Apriori.R		Apriori.R
Artificial Neural Network.ipynb		Artificial Neural Network.ipynb
Artificial Neural Networks.R		Artificial Neural Networks.R
Categorical Data.R		Categorical Data.R
Convolutional Neural Network.ipynb		Convolutional Neural Network.ipynb
Data Preprocessing Data Section 2.ipynb		Data Preprocessing Data Section 2.ipynb
Decision Tree Classification.R		Decision Tree Classification.R
Decision Tree Classification.ipynb		Decision Tree Classification.ipynb
Decision Tree Regression .ipynb		Decision Tree Regression .ipynb
Decision Tree Regression.R		Decision Tree Regression.R
Grid Search.R		Grid Search.R
Grid Search.ipynb		Grid Search.ipynb
Hierarchical Clustering in Python.ipynb		Hierarchical Clustering in Python.ipynb
Hierarchical Clustering in R.R		Hierarchical Clustering in R.R
K-Means Clustering Lecture.R		K-Means Clustering Lecture.R
K-Means Clustering Lecture.ipynb		K-Means Clustering Lecture.ipynb
Kernel PCA.R		Kernel PCA.R
Kernel PCA.ipynb		Kernel PCA.ipynb
Kernel SVM.R		Kernel SVM.R
Kernel SVM.ipynb		Kernel SVM.ipynb
Linear Discriminant Analysis (LDA).R		Linear Discriminant Analysis (LDA).R
Linear Discriminant Analysis (LDA).ipynb		Linear Discriminant Analysis (LDA).ipynb
Logistic Regression Lecture.R		Logistic Regression Lecture.R
Logistic Regression.ipynb		Logistic Regression.ipynb
Market_Basket_Optimisation.csv		Market_Basket_Optimisation.csv
Missing Data.ipynb		Missing Data.ipynb
Multiple Linear Regression Lecture and HW.R		Multiple Linear Regression Lecture and HW.R
Naive Bayes.R		Naive Bayes.R
Naive Bayes.ipynb		Naive Bayes.ipynb
Natural Language Processing.ipynb		Natural Language Processing.ipynb
Polynomial Regression Lecture.R		Polynomial Regression Lecture.R
Polynomial Regression Lecture.ipynb		Polynomial Regression Lecture.ipynb
Principal Component Analysis.R		Principal Component Analysis.R
Principal Component Analysis.ipynb		Principal Component Analysis.ipynb
README.md		README.md
Random Forest Classification Lecture.R		Random Forest Classification Lecture.R
Random Forest Classification.ipynb		Random Forest Classification.ipynb
Random Forest Regression.R		Random Forest Regression.R
Random Forest Regression.ipynb		Random Forest Regression.ipynb
Restaurant_Reviews.tsv		Restaurant_Reviews.tsv
SVM Lecture.R		SVM Lecture.R
SVM Lecture.ipynb		SVM Lecture.ipynb
SVR.R		SVR.R
Simple Linear Regression.R		Simple Linear Regression.R
Simple Linear Regression.ipynb		Simple Linear Regression.ipynb
Social_Network_Ads.csv		Social_Network_Ads.csv
Support Vector Regression Lecture.ipynb		Support Vector Regression Lecture.ipynb
Thompson Sampling Lecture.ipynb		Thompson Sampling Lecture.ipynb
Upper Confidence Bound .ipynb		Upper Confidence Bound .ipynb
Upper Confidence Bound.R		Upper Confidence Bound.R
eclat.R		eclat.R
missing_data.R		missing_data.R
natural_language_processing.R		natural_language_processing.R
thompson_sampling.R		thompson_sampling.R
upper_confidence_bound.R		upper_confidence_bound.R
xgboost.R		xgboost.R
xgboost.py		xgboost.py

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