A collection of notebooks on Machine Learning

Descriptive notebooks on how several machine learning algorithms work. Numerical and code examples are provided for the following:

• Gradient Descent
• Linear Regression
• Logistic Regression
• Rosenblat's Perceptron
• Support Vector Machines
• Multilayer Perceptron - Feed Forward Neural Networks
• k-Means
• Decision Trees
• Random Forests

Notebooks on data (pre)processing:

• Principal Component Analysis
• Dataset Handling
• Dataset preprocessing
• Text datasets
• Clustering with Text Datasets

This research is co-financed by Greece and the European Union (European Social Fund-SF) through the Operational Programme ``Human Resources Development, Education and Lifelong Learning 2014-2020'' in the context of the project "Support for International Actions of the International Hellenic University" (MIS 5154651).