Gacovski Z. (ed.) Soft Computing and Machine Learning with Python

Arcler Press, 2019. — 380 p. — ISBN: 978-1-77361-623-0.Soft computing and machine learning with python examines various aspects of machine learning with python with a detailed information on soft computing. It includes four different sections, where section 1 and 2 are dedicated towards soft computing theory and machine learning techniques and on the other hand section 3 and 4 are dedicated to the details of python language and machine learning with python. The book provides the reader with the insights into the development of python and machine learning, soas to understand the classification multigraph models of secondary RNA structure using graph-theoretic descriptors.Soft Computing Theory
Machine Learning Overview
Types of Machine Learning Algorithms
Data Mining With Skewed Data
Machine Learning Techniques and Applications
Survey of Machine Learning Algorithms For Disease Diagnostic
Bankruptcy Prediction Using Machine Learning
Prediction of Solar Irradiation Using Quantum Support Vector
Machine Learning Algorithm
Predicting Academic Achievement of High-School Students
Using Machine Learning
Python Language Details
A Python 2.7 Programming Tutorial
Pattern For Python
Pystruct - Learning Structured Prediction In Python
Machine Learning with Python
Python Environment For Bayesian Learning: Inferring The Structure
of Bayesian Networks From Knowledge And Data
An Efficient Platform For The Automatic Extraction of Patterns in Native Code.
Polyglot Programming In Applications Used For Genetic Data Analysis
Classifying Multigraph Models Of Secondary RNA Structure Using Graph-Theoretic Descriptors