Manufacturing industry Introduction to Data Analysis and Machine Learning in Python for Chemistry (2nd revised edition) / Hiromasa Kaneko

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Manufacturing industry
[Introduction to Contents]
Introduction to Python-based Machine Learning in the Field of Chemical and Chemical Engineering
This guide is an introduction to Python-based machine learning in the field of Chemical and Chemical Engineering.
This guide is an introduction to Python-based machine learning in the field of Chemical and Chemical Engineering.
By using data analysis and machine learning to analyze experimental and manufacturing data accumulated so far, it is possible to accelerate material development and make process management more efficient and stable. This is because experiments and manufacturing data are rich in knowledge, knowledge, experience, and intuition of researchers and engineers, which are not visible to the naked eye. By using data analysis and machine learning, it is possible to make them visible.
This guide carefully explains how to install Python, the basic theory of data analysis and machine learning, and how to actually practice material design, molecular design, and process management using sample programs and sample datasets.
Contents
Part 1 : Introduction to Python and Statistics
Chapter 1 : Introduction to Python
Chapter 2 : Introduction to Data Analysis and Machine Learning
Part 2 : Introduction to Data Analysis and Machine Learning
Chapter 3 : Multivariate Data and Data Visualization
Chapter 4 : Modeling Using Chemical Data
Chapter 5 : Application of Regression Models and Classification Models
Part 3 : Application of Regression Models and Classification Models
Chapter 6 : Materials Design, Molecular Design, Pharmaceutical Design
Chapter 7 : Analysis of Time Series
Chapter 8 : Datachemical LAB-based Analysis and Machine Learning