Data Science and Engineering - A learning path – Vocabulary of Machine Learning Algorithms: with applications in the Orange Python-based environment

By Mario A.B. Capurso

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. The goal is to build a vocabulary of machine learning algorithms, divided by the main learning methods. The text describes and delves into over 110 machine learning algorithms and architectures, reporting their description, technology, limitations, strengths, weaknesses and bibliographical references. An example of use is also given for some of them. Part of a series of books, it first summarizes the standard CRISP DM working methodology used in this work and in Data Science projects. Since this text uses Orange for application aspects, it describes its installation and widgets. Next we consider the concept of the model, its life cycle and the relationship with measures and metrics. Then, the data modeling phase is considered from the perspective of machine learning by delving into the types of machine learning, types of models, types of problems and types of algorithms. The algorithms are listed by classifying them into supervised, unsupervised, reinforcement, association, ensemble and deep learning. After considering the main classes of learning algorithms, the ideal characteristics of models and algorithms are explored, a vocabulary of model types and algorithms is compiled, and their use in Orange across various types of projects is considered. The text is accompanied by supporting material and you can download the samples in Orange and the test data.

• Format: Paperback
• Language: English
• ISBN: B0DBF5XN9V
• ISBN13: 9798334280472
• Release Date: July 2024
• Publisher: Independently published
• Length: 492 Pages
• Weight: 2.38 lbs.
• Dimensions: 9.7" x 1.1" x 7.4"