Om den här e-boken
Part in a series of texts, it first summarizes the standard CRISP DM working methodology used in this work and in Data Science projects. As this text uses Orange for the application aspects, it describes its installation and widgets.
The data modeling phase is considered from the perspective of machine learning by summarizing machine learning types, model types, problem types, and algorithm types.
Advanced aspects associated with modeling are described such as loss and optimization functions such as gradient descent, techniques to analyze model performance such as Bootstrapping and Cross Validation. Deployment scenarios and the most common platforms are analyzed, with application examples. Mechanisms are proposed to automate machine learning and to support the interpretability of models and results such as Partial Dependence Plot, Permuted Feature Importance and others.
The exercises are described with Orange and Python using the Keras/Tensorflow library.
The text is accompanied by supporting material and it is possible to download the examples and the test data.
Om författaren
Graduated in Computer Science, has taught Computer Science, Formal Languages and Compilers at the University of Bari at the Faculty of Computer Science and Fundamentals of Computer Science II at the Polytechnic of Bari in the Degree course in Electronic Engineering
He has also worked for over twenty years in various companies also in the field of Data Science
