Data Science Quick Reference Manual - Advanced Machine Learning and Deployment: with exercises in Orange and Python

Mario A. B. Capurso

Data Science Quick Reference Manual Sách 6 · Mario Capurso

Giới thiệu về sách điện tử này

This work follows the 2021 curriculum of the Association for Computing Machinery for specialists in Data Sciences, with the aim of producing a manual that collects notions in a simplified form, facilitating a personal training path starting from specialized skills in Computer Science or Mathematics or Statistics. It has a bibliography with links to quality material but freely usable for your own training and contextual practical exercises.

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.

Giới thiệu tác giả

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