Machine Learning. Supervised Learning Techniques and Tools Nonlinear Models Exercises with R, SAS, STATA, EVIEWS and SPSS

BOOKS - Machine Learning. Supervised Learning Techniques and Tools Nonlinear Models E...

Machine Learning. Supervised Learning Techniques and Tools Nonlinear Models Exercises with R, SAS, STATA, EVIEWS and SPSS - Cesar Perez Lopez 2024 EPUB Scientific Books BOOKS

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Machine Learning. Supervised Learning Techniques and Tools Nonlinear Models Exercises with R, SAS, STATA, EVIEWS and SPSS

Author: Cesar Perez Lopez
Year: 2024
Format: EPUB
File size: 11.5 MB
Language: ENG

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The book "Machine Learning Supervised Learning Techniques and Tools Nonlinear Models Exercises with R SAS STATA EVIEWS and SPSS" is a comprehensive guide to understanding the latest advancements in machine learning and its applications in various fields. The book covers the fundamental concepts of supervised learning techniques and tools, nonlinear models, and exercises using popular software such as R, SAS, STATA, EVIEWS, and SPSS. It provides a detailed overview of the current state of technology and its impact on society, emphasizing the importance of developing a personal paradigm for perceiving the technological process of developing modern knowledge as the basis for the survival of humanity and the survival of the unification of people in a warring state. The book begins by introducing the concept of machine learning and its significance in today's world. It highlights the need to understand the process of technology evolution and its impact on society, as well as the importance of developing a personal paradigm for perceiving the technological process of developing modern knowledge. The author argues that this is essential for the survival of humanity and the survival of the unification of people in a warring state. The book then delves into the fundamentals of supervised learning techniques and tools, including linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks. Each chapter provides a detailed explanation of the concepts, along with practical exercises using real-world datasets to reinforce the understanding of the topics. The exercises are designed to help readers apply their knowledge of machine learning algorithms to solve real-world problems.

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