Introduction to Machine Learning and Neural Classification (eBook, ePUB)
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Introduction to Machine Learning and Neural Classification is your gateway to understanding the fundamental aspects of machine learning, a subset of AI, as well as neural networks and statistical classification. As machine learning becomes increasingly integral to our lives, this book covers every significant topic with clarity and precision. We start with an introduction to key terms like Data Science, Machine Learning, Data Mining, Neural Networks, and Statistical Classification. We then explore classical and modern statistical techniques and methods. The book dives into decision tree rules ...
Introduction to Machine Learning and Neural Classification is your gateway to understanding the fundamental aspects of machine learning, a subset of AI, as well as neural networks and statistical classification. As machine learning becomes increasingly integral to our lives, this book covers every significant topic with clarity and precision.
We start with an introduction to key terms like Data Science, Machine Learning, Data Mining, Neural Networks, and Statistical Classification. We then explore classical and modern statistical techniques and methods. The book dives into decision tree rules in machine learning and covers neural networks, including methods of comparison and empirical analysis.
Readers will also learn about descriptive statistics, knowledge representation, control dynamic systems, and data mining algorithms. Throughout the book, explanatory diagrams, bar graphs, and tables are provided to enhance understanding.
Whether you're a beginner or looking to deepen your knowledge, this book provides comprehensive insights into these fascinating topics, making it an essential read for anyone interested in machine learning and AI.
We start with an introduction to key terms like Data Science, Machine Learning, Data Mining, Neural Networks, and Statistical Classification. We then explore classical and modern statistical techniques and methods. The book dives into decision tree rules in machine learning and covers neural networks, including methods of comparison and empirical analysis.
Readers will also learn about descriptive statistics, knowledge representation, control dynamic systems, and data mining algorithms. Throughout the book, explanatory diagrams, bar graphs, and tables are provided to enhance understanding.
Whether you're a beginner or looking to deepen your knowledge, this book provides comprehensive insights into these fascinating topics, making it an essential read for anyone interested in machine learning and AI.
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