| 1 |
Data Preprocessing |
Files in Python and R that handles missing data, transforms categorical data into numerical data, applies simple feature scaling techniques and splits data into training and test sets. |
| 2 |
Regression |
Files in Python and R that shows how to perform the different types of regression techniques in ML: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, Support Vector Regression (SVR), Decision Tree Regression, Random Forest Regression. |
| 3 |
Classification |
Files in Python and R that show how to perform the different types of classification techniques in ML: Logistic Regression, KNN-algorithm, Support Vector Machines, Kernel SVM (non linear problems), Naive Bayes, Decision Tree Classification, Random Forest Classification. |
| 4 |
Clustering |
Files in Python and R that show how to perform the different types of clustering techniques in ML: hierarchical clustering and k_means. |
| 5 |
Association Rule |
File in Python and 2 files R that show how to perform the Eclat and Apriori techniques. |
| 6 |
Reinforcement Learning |
Files in Python and R that show how to perform the different types of reinforcement learning techniques in ML: Thompson learning and Upper Confidence Bound. |
| 7 |
Natural Language Processing |
Files in Python and R that guide you to perform basic natural language processing techniques. |
| 8 |
Deep Learning |
Files in Python and R that show how to create an Artificial Neural Network and a file in Python that guide you to create a convolutionary neural network. The files for the convolutionary neural network were not uploaded because it weights too much. |
| 9 |
Dimensionality Reduction |
Files in Python and R that show how to apply the Principal Component Analysis and Linear Discriminant Analysis techniques. |
| 10 |
Model Selection and Boosting |
Files in Python that show how to apply the tuning techniques of grid search and k_fold_cross_validation to find the best parameters in regression or classification methods. |