ML is a repository that contains supervised learning algorithm templates. The algorithms are divided by regression and classification, as shown below in "List of Algorithms".

Each algorithm is divided in different sections in Jupyter Notebook with appropriate instructions. None of the sections needs to be adjusted but the data input and input/target variables definition. Note that all algorithms use one dataset as an example. Therefore, in some algorithms, the quality of the input and target variables of being suitable or proper in the circumstances might be very low.

The author is responsible for the content and quality of the code. Please refer to The Learning Machine (thelearningmachine.ai) for any remarks.

• Neural Network Classifier
• Linear Support Vector Machines (LSVM)
• Logistic Regression
• MultiLayer Perceptron (MLP)
• Kernelized Support Vector Machines (SVM)
• Decision Trees
• Naive Bayes Classifiers
• Random Forests
• Recurrent Neural Network (RNN): Applied to MNIST Classification Task

• Neural Network Regressor
• Ridge Regression
• Lasso Regression
• Polynomial Regression
• Linear Regression