Soft Computing and Machine Learning with Python

Soft Computing and Machine Learning with Python
PDF
  • eBook:
    Soft Computing and Machine Learning with Python
  • Author:
    Zoran Gacovski
  • Edition:
    Edition 2019
  • Categories:
  • Data:
    December 1, 2018
  • ISBN:
    1773615009
  • ISBN-13:
    9781773615004
  • Language:
    English
  • Pages:
    378 pages
  • Format:
    PDF

Book Description
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. Provides the reader with the insights into the development of python and machine learning, so as to understand the classification multigraph models of secondary RNA structure using graph-theoretic descriptors.

Content

SECTION I - SOFT COMPUTING THEORY
Chapter 1. Machine Learning Overview
Chapter 2. Types of Machine Learning Algorithms
Chapter 3. Data Mining With Skewed Data

SECTION II - MACHINE LEARNING TECHNIQUES AND APPLICATIONS
Chapter 4. Survey of Machine Learning Algorithms For Disease Diagnostic
Chapter 5. Bankruptcy Prediction Using Machine Learning
Chapter 6. Prediction of Solar Irradiation Using Quantum Support Vector Machine Learning Algorithm
Chapter 7. Predicting Academic Achievement of High-School Students Using Machine Learning

SECTION III - PYTHON LANGUAGE DETAILS
Chapter 8. A Python 2.7 Programming Tutorial
Chapter 9. Pattern For Python
Chapter 10. Pystruct - Learning Structured Prediction In Python

SECTION IV - MACHINE LEARNING WITH PYTHON
Chapter 11. Python Environment For Bayesian Learning: Inferring The Structure of Bayesian Networks From Knowledge And Data
Chapter 12. Scikit-Learn: Machine Learning In Python
Chapter 13. An Efficient Platform For The Automatic Extraction of Patterns in Native Code
Chapter 14. Polyglot Programming In Applications Used For Genetic Data Analysis
Chapter 15. Classifying Multigraph Models Of Secondary RNA Structure Using Graph-Theoretic Descriptors

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