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).