Data Science and Machine Learning for Non-Programmers: Using SAS Enterprise MinerКНИГИ » ПРОГРАММИНГ
Название: Data Science and Machine Learning for Non-Programmers: Using SAS Enterprise Miner Автор: Dothang Truong Издательство: CRC Press Год: 2024 Страниц: 590 Язык: английский Формат: pdf (true) Размер: 35.9 MB
As data continues to grow exponentially, knowledge of Data Science and Machine Learning has become more crucial than ever. Machine Learning has grown exponentially; however, the abundance of resources can be overwhelming, making it challenging for new learners. This book aims to address this disparity and cater to learners from various non-technical fields, enabling them to utilize Machine Learning effectively.
Adopting a hands-on approach, readers are guided through practical implementations using real datasets and SAS Enterprise Miner, a user-friendly data mining software that requires no programming. Throughout the chapters, two large datasets are used consistently, allowing readers to practice all stages of the data mining process within a cohesive project framework. This book also provides specific guidelines and examples on presenting data mining results and reports, enhancing effective communication with stakeholders.
In recent years, the field of Machine Learning has seen an unprecedented surge in popularity and innovation, which has revolutionized industries and transformed the way we interact with technology. In this rapidly evolving field, the pursuit of knowledge hidden within vast datasets has led to groundbreaking discoveries, insightful patterns, and novel applications across diverse domains. It is an exciting time as we find ourselves immersed in the realm of true Artificial Intelligence (AI). I still vividly recall my amazement when I had a chance to test out Tesla’s full self-driving technology last year. The car effortlessly drove itself from a hotel to the airport, leaving a lasting impression of the limitless potential of AI. More recently, as I was finishing the final chapter of this book, ChatGPT was introduced. Honestly, I was a little bit skeptical at first, given some other applications that claimed to be true AI before, and then disappointed. Regardless, I decided to test it out anyway, and I was thoroughly surprised. What a game changer! The capability of this generative AI goes beyond anyone’s imagination. A few months later, when my book was completed, ChatGPT had already evolved so much. I use it frequently for different tasks, and it gets better every day. With each passing day, ChatGPT continues to evolve, further affirming its capacity to learn and adapt independently. I firmly believe that this technology is a breakthrough that takes us closer to true AI.
The book begins with Part I, introducing the core concepts of data science, data mining, and Machine Learning. My aim is to present these principles without overwhelming readers with complex math, empowering them to comprehend the underlying mechanisms of various algorithms and models. This foundational knowledge will enable readers to make informed choices when selecting the right tool for specific problems. In Part II, I focus on the most popular Machine Learning algorithms, including regression methods, decision trees, neural networks, ensemble modeling, principal component analysis, and cluster analysis. Once readers feel confident with these methods, Part III introduces more advanced techniques, such as random forest, gradient boosting, and Bayesian networks, enabling them to build more complicated Machine Learning models. These principles and methods are beneficial for learners from all backgrounds. If you come from a non-technical background (business, finance, marketing, aviation, and social science), you will enjoy the simple explanations of the Machine Learning methods and the tips I provide. If you come from a technical background (Computer Science, statistics, and engineering), these chapters offer insights into fundamental concepts and principles of Machine Learning without delving into intricate mathematical theorems and formulas.
Designed as a guiding companion for both beginners and experienced practitioners, this book targets a wide audience, including students, lecturers, researchers, and industry professionals from various backgrounds.
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Data Science and Machine Learning for Non-Programmers: Using SAS Enterprise Miner 
