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5 books on Machine Learning [PDF]
Updated: May 12, 2024 | 22 |
Books on Machine Learning are invaluable resources for startups specializing in Machine Learning technologies. These resources provide a comprehensive foundation in the field, covering various aspects of machine learning, including supervised and unsupervised learning, deep learning, and reinforcement learning. They delve into the intricacies of algorithm design, feature engineering, and model evaluation, emphasizing the importance of data-driven decision-making and predictive analytics. Moreover, these books often include practical examples, datasets, and best practices, enabling startups to build and fine-tune their machine learning models for real-world applications.
1. Machine Learning For Dummies
2021 by John Paul Mueller, Luca Massaron
In contrast to many other machine learning guides, "Machine Learning For Dummies" doesn't assume a prior background in programming languages like Python (it also includes R source code for download with explanatory comments). Instead, it starts from the ground up, covering fundamental concepts for those looking to begin building practical models without extensive experience. While it explores the intriguing mathematical principles underlying machine learning, it reassures readers that they don't need to be math experts to create exciting new tools and apply them to their work and studies. This book delves into the history of AI and machine learning, provides hands-on experience with Python 3.8 and TensorFlow 2.x (along with R source code), guides readers in constructing and testing their own models, utilizes the latest datasets, and demonstrates the application of machine learning to real-world problems. Whether you're learning for academic purposes or aiming to enhance your professional or business performance, this approachable beginner's guide serves as an ideal introduction to the world of machine learning, enabling you to quickly gain confidence in harnessing this transformative technology that's making a positive impact worldwide.
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2. Interpretable Machine Learning
2020 by Christoph Molnar
"This book delves into the realm of interpretability in machine learning, with a primary focus on enhancing the transparency of machine learning models and their decision-making processes. Beginning with an exploration of interpretability concepts, it gradually introduces readers to straightforward and easy-to-comprehend models like decision trees, decision rules, and linear regression. Subsequent chapters delve into more advanced model-agnostic techniques for deciphering the inner workings of black box models. These methods encompass feature importance assessment, accumulated local effects analysis, as well as the clarification of individual predictions through the utilization of Shapley values and LIME. The book not only elucidates the operational principles of these interpretation methods but also critically evaluates their performance, strengths, weaknesses, and how to decipher their outputs. By the end of this book, readers will be equipped to select and effectively apply the most suitable interpretation method tailored to their specific machine learning projects."
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3. Machine Learning Algorithms
2017 by Giuseppe Bonaccorso
"Machine Learning Algorithms" is a robust and conceptually rich guide, offering practical insights into the world of machine learning algorithms. It serves as a comprehensive resource, providing you with essential knowledge on the principles and applications of machine learning algorithms. This book is designed for IT professionals seeking entry into the field of data science, especially those who are new to machine learning. It assumes a foundational familiarity with programming languages such as R and Python, which proves invaluable throughout the journey. With this guide, you'll gain a solid understanding of key machine learning components, delve into feature selection and engineering, navigate performance assessment and error trade-offs in Linear Regression, construct data models employing various algorithms, fine-tune Support Vector Machines, implement clustering techniques, explore Natural Language Processing and Recommendation Systems, and even craft a machine learning architecture from the ground up.
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4. Understanding Machine Learning: From Theory to Algorithms
2014 by Shai Shalev-Shwartz, Shai Ben-David
The primary goal of this textbook is to provide a structured introduction to the world of machine learning, along with its associated algorithmic principles, in a methodical manner. It offers a comprehensive theoretical exploration of the foundational concepts that underpin machine learning, elucidating the mathematical derivations that transform these principles into pragmatic algorithms. Commencing with a foundational overview of the field, the book encompasses a broad spectrum of pivotal subjects that have remained relatively unaddressed in previous textbooks. These encompass discussions on the computational intricacies of learning, as well as concepts like convexity and stability. The book further delves into crucial algorithmic paradigms such as stochastic gradient descent, neural networks, and structured output learning. Additionally, it explores emerging theoretical notions such as the PAC-Bayes approach and compression-based bounds. Tailored for an advanced undergraduate or introductory graduate course, this text ensures that the fundamentals and algorithms of machine learning are made accessible to students and individuals with diverse backgrounds in statistics, computer science, mathematics, and engineering.
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5. Machine Learning: The Art and Science of Algorithms that Make Sense of Data
2012 by Peter Flach
Among the most comprehensive texts on machine learning, this book embraces the remarkable breadth of the field while maintaining a focus on its fundamental principles. Peter Flach adopts a lucid, example-driven approach that initiates with the mechanics of a spam filter, providing an immediate immersion into machine learning in practical contexts, with minimal technical complexity. The book proceeds to present case studies of increasing intricacy and diversity, replete with well-chosen illustrations and examples. Flach encompasses an array of logical, geometric, and statistical models, alongside cutting-edge topics like matrix factorization and ROC analysis. Special emphasis is accorded to the pivotal role of features in the process. The book strikes a balance between established terminology and the introduction of innovative and valuable concepts, ensuring that readers have access to both familiar and new perspectives. Additionally, it offers summaries of pertinent background material with references for further exploration if required. With these attributes, "Machine Learning" sets a new benchmark as an introductory textbook in the field.
Download PDF
How to download PDF:
1. Install Google Books Downloader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
1. Machine Learning For Dummies
2021 by John Paul Mueller, Luca Massaron
In contrast to many other machine learning guides, "Machine Learning For Dummies" doesn't assume a prior background in programming languages like Python (it also includes R source code for download with explanatory comments). Instead, it starts from the ground up, covering fundamental concepts for those looking to begin building practical models without extensive experience. While it explores the intriguing mathematical principles underlying machine learning, it reassures readers that they don't need to be math experts to create exciting new tools and apply them to their work and studies. This book delves into the history of AI and machine learning, provides hands-on experience with Python 3.8 and TensorFlow 2.x (along with R source code), guides readers in constructing and testing their own models, utilizes the latest datasets, and demonstrates the application of machine learning to real-world problems. Whether you're learning for academic purposes or aiming to enhance your professional or business performance, this approachable beginner's guide serves as an ideal introduction to the world of machine learning, enabling you to quickly gain confidence in harnessing this transformative technology that's making a positive impact worldwide.
Download PDF
2. Interpretable Machine Learning
2020 by Christoph Molnar
"This book delves into the realm of interpretability in machine learning, with a primary focus on enhancing the transparency of machine learning models and their decision-making processes. Beginning with an exploration of interpretability concepts, it gradually introduces readers to straightforward and easy-to-comprehend models like decision trees, decision rules, and linear regression. Subsequent chapters delve into more advanced model-agnostic techniques for deciphering the inner workings of black box models. These methods encompass feature importance assessment, accumulated local effects analysis, as well as the clarification of individual predictions through the utilization of Shapley values and LIME. The book not only elucidates the operational principles of these interpretation methods but also critically evaluates their performance, strengths, weaknesses, and how to decipher their outputs. By the end of this book, readers will be equipped to select and effectively apply the most suitable interpretation method tailored to their specific machine learning projects."
Download PDF
3. Machine Learning Algorithms
2017 by Giuseppe Bonaccorso
"Machine Learning Algorithms" is a robust and conceptually rich guide, offering practical insights into the world of machine learning algorithms. It serves as a comprehensive resource, providing you with essential knowledge on the principles and applications of machine learning algorithms. This book is designed for IT professionals seeking entry into the field of data science, especially those who are new to machine learning. It assumes a foundational familiarity with programming languages such as R and Python, which proves invaluable throughout the journey. With this guide, you'll gain a solid understanding of key machine learning components, delve into feature selection and engineering, navigate performance assessment and error trade-offs in Linear Regression, construct data models employing various algorithms, fine-tune Support Vector Machines, implement clustering techniques, explore Natural Language Processing and Recommendation Systems, and even craft a machine learning architecture from the ground up.
Download PDF
4. Understanding Machine Learning: From Theory to Algorithms
2014 by Shai Shalev-Shwartz, Shai Ben-David
The primary goal of this textbook is to provide a structured introduction to the world of machine learning, along with its associated algorithmic principles, in a methodical manner. It offers a comprehensive theoretical exploration of the foundational concepts that underpin machine learning, elucidating the mathematical derivations that transform these principles into pragmatic algorithms. Commencing with a foundational overview of the field, the book encompasses a broad spectrum of pivotal subjects that have remained relatively unaddressed in previous textbooks. These encompass discussions on the computational intricacies of learning, as well as concepts like convexity and stability. The book further delves into crucial algorithmic paradigms such as stochastic gradient descent, neural networks, and structured output learning. Additionally, it explores emerging theoretical notions such as the PAC-Bayes approach and compression-based bounds. Tailored for an advanced undergraduate or introductory graduate course, this text ensures that the fundamentals and algorithms of machine learning are made accessible to students and individuals with diverse backgrounds in statistics, computer science, mathematics, and engineering.
Download PDF
5. Machine Learning: The Art and Science of Algorithms that Make Sense of Data
2012 by Peter Flach
Among the most comprehensive texts on machine learning, this book embraces the remarkable breadth of the field while maintaining a focus on its fundamental principles. Peter Flach adopts a lucid, example-driven approach that initiates with the mechanics of a spam filter, providing an immediate immersion into machine learning in practical contexts, with minimal technical complexity. The book proceeds to present case studies of increasing intricacy and diversity, replete with well-chosen illustrations and examples. Flach encompasses an array of logical, geometric, and statistical models, alongside cutting-edge topics like matrix factorization and ROC analysis. Special emphasis is accorded to the pivotal role of features in the process. The book strikes a balance between established terminology and the introduction of innovative and valuable concepts, ensuring that readers have access to both familiar and new perspectives. Additionally, it offers summaries of pertinent background material with references for further exploration if required. With these attributes, "Machine Learning" sets a new benchmark as an introductory textbook in the field.
Download PDF
How to download PDF:
1. Install Google Books Downloader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
