"Introduction to Data Science and Machine Learning" ed. by Keshav Sud, Pakize Erdogmus, Seifedine Kadry

This volume has been created with the goal to provide beginners seeking to learn about data science, data enthusiasts, and experienced data professionals with a deep understanding of data science application development using open-source programming from start to finish.

This book is divided into four sections: the first section contains an introduction to the book, the second covers the field of data science, software development, and open-source based embedded hardware; the third section covers algorithms that are the decision engines for data science applications; and the final section brings together the concepts shared in the first three sections and provides several examples of data science applications.


Contents
1.Introductory Chapter: Clustering with Nature-Inspired Optimization Algorithms
2.Best Practices in Accelerating the Data Science Process in Python
3.Software Design for Success
4.Embedded Systems Based on Open Source Platforms
5.The K-Means Algorithm Evolution
6.“Set of Strings” Framework for Big Data Modeling
7.Investigation of Fuzzy Inductive Modeling Method in Forecasting Problems
8.Segmenting Images Using Hybridization of K-Means and Fuzzy C-Means Algorithms
9.The Software to the Soft Target Assessment
10.The Methodological Standard to the Assessment of the Traffic Simulation in Real Time
11.Augmented Post Systems: Syntax, Semantics, and Applications
12.Serialization in Object-Oriented Programming Languages