Giuseppe Ciaburro Boeken

This book focuses on constructing advanced simulation models using Python, equipping readers with the skills to enhance their simulation modeling capabilities. It guides users in creating and analyzing digital prototypes of physical models, making complex concepts accessible and practical for various applications. Ideal for both beginners and experienced practitioners, it emphasizes hands-on learning and real-world examples to facilitate understanding and mastery of simulation techniques.

Focusing on practical applications, this book guides readers in mastering MATLAB tools for machine learning. It emphasizes effective code writing and includes numerous examples that demonstrate the versatility of machine learning in real-world scenarios. Readers will explore the MATLAB Machine Learning Toolbox, learning to implement a range of algorithms to enhance their understanding and skills in developing machine learning applications.

Extract patterns and knowledge from your data easily using MATLAB. This guide introduces machine learning concepts, covering regression, clustering, classification, predictive analytics, and artificial neural networks. It is designed for data analysts, scientists, students, or anyone eager to delve into machine learning and develop efficient data processing applications. A background in mathematics and statistics will enhance comprehension. You will learn the fundamentals of machine learning, data transformation techniques, and how to use SAS XPORT for data handling. The book explores various regression techniques, including simple and multiple linear regression, ordinary least squares estimation, and correlations. It also covers classification methods, implementing algorithms like Naive Bayes and Decision Trees in MATLAB. Clustering techniques, such as hierarchical clustering, are discussed for data grouping based on similarity measures. The MATLAB Neural Network Toolbox is introduced for data fitting, pattern recognition, and clustering analysis. You'll learn about feature selection and extraction for dimensionality reduction, improving performance. The book provides a solid foundation in machine learning with MATLAB, guiding you through system setup, data cleansing, mining, and analysis. Real-world examples and use cases support your understanding, enabling you to apply concepts effectively in your work. By the end, you'l