| title | excerpt | publishDate | category |
|---|---|---|---|
The Best Machine Learning Books |
Discover the top recommended machine learning books to enhance your knowledge and practical skills in the field, covering essential concepts and advanced techniques. |
2024-05-18T09:50:38Z |
Dive into the fascinating world of machine learning with our curated collection of top books in the field. From beginners to experts, we've got you covered with a variety of resources that explore the latest techniques and applications of this rapidly evolving field. Explore the best books on machine learning that will help you gain a deeper understanding and become a master in your domain.
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"Readings in Machine Learning: The Essential Collection" is an invaluable resource for individuals who wish to gain a deep understanding of machine learning and its applications. The book, published in 1990 by Morgan Kaufmann, provides an accessible window into the world of machine learning, one that is rich with insights, practical applications, and innovative techniques.
In "Readings in Machine Learning, " editors Jude W. Shavlik and Thomas Glen Dietterich have meticulously curated a selection of groundbreaking works, including empirical studies, theoretical analyses, and psychological justifications. These papers are organized into a dozen carefully selected categories, each introducing readers to the interdisciplinary nature of machine learning and its far-reaching impact on science, engineering, and business.
With a total of 868 pages, this comprehensive collection offers an unparalleled exploration of machine learning, delving into the core concepts, methods, and challenges that have shaped the field's development since its inception in the 1950s. By focusing on important papers that have stood the test of time, "Readings in Machine Learning" establishes itself as an indispensable resource that continues to resonate within the AI community.
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Authors: Jude W. Shavlik, Thomas Glen Dietterich
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Publisher: Morgan Kaufmann
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Published Date: March 30, 1990
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Page Count: 868
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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Machine Learning and Data Mining provides a comprehensive introduction to the crucial basics of machine learning and the algorithms employed in data mining. Perfect for advanced undergraduates, postgraduates, and tutors in computer science and technology, this book also appeals to researchers seeking to adapt various algorithms for specific data mining tasks. The text showcases the application of data mining practices in business intelligence and presents numerous techniques and developments in the conversion of scientific discoveries into widely accessible software solutions.
Suitable for a diverse range of readers, Machine Learning and Data Mining has become an indispensable resource for companies leveraging the principles of data mining to deliver cutting-edge business and industry solutions.
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Authors: Igor Kononenko, Matjaz Kukar
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Publisher: Horwood Publishing
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Published Date: April 30, 2007
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Page Count: 484
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Print Type: BOOK
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Categories: Computers
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Average Rating: 3.0
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Ratings Count: 1.0
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Maturity Rating: NOT_MATURE
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Language: en
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Unlock the vast world of Machine Learning with the Encyclopedia of Machine Learning by Claude Sammut and Geoffrey I. Webb. This comprehensive encyclopedia, published by Springer Science & Business Media, is a go-to resource for anyone interested in venturing into the ever-expanding field of Machine Learning. Organized in an easy-to-use A-Z format, it covers all the essential topics and subtopics, making it a must-have for beginners and seasoned professionals alike.
Each entry in this preeminent work is designed to provide readers with a concise yet informative understanding of the subject matter. Moreover, the authors have included an array of literature references, enabling readers to delve deeper into the topic of their choice.
The Encyclopedia of Machine Learning is a valuable addition to any library, and with its thorough coverage, it serves as an indispensable guide for students, researchers, and professionals in the ever-evolving field of Machine Learning. Don't miss the opportunity to acquaint yourself with this remarkable resource – join the forefront of Machine Learning today!
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Authors: Claude Sammut, Geoffrey I. Webb
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Publisher: Springer Science & Business Media
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Published Date: March 28, 2011
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Page Count: 1061
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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An Artificial Intelligence Approach
Unleash the power of artificial intelligence and explore the intricacies of machine learning with "Machine Learning: An Artificial Intelligence Approach" by R. S. Michalski, J. G. Carbonell, and T. M. Mitchell. As the first available text on this subject, this comprehensive book delves into the world of intelligent behavior and its significance across various disciplines.
This 564-page masterpiece offers captivating tutorial overviews and thought-provoking research papers, providing an in-depth perspective on contemporary trends in machine learning from an artificial intelligence standpoint. Whether you're a seasoned professional or a curious beginner, "Machine Learning" promises to ignite your passion for this burgeoning field of study.
Join the esteemed authors as they guide you through the intricacies of learning processes and share their insights on how machines might be endowed with the ability to learn. This book is an essential addition to the library of anyone interested in shaping the future of artificial intelligence and machine learning. Don't miss your chance to be part of the evolution - grab your copy of "Machine Learning: An Artificial Intelligence Approach" today!
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Authors: R.S. Michalski, J.G. Carbonell, T.M. Mitchell
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Publisher: Springer Science & Business Media
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Published Date: April 17, 2013
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Page Count: 564
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Print Type: BOOK
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Categories: Computers
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Average Rating: 2.0
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Ratings Count: 1.0
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Maturity Rating: NOT_MATURE
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Language: en
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"Learn Windows IIS in a Month of Lunches" is a concise and easy-to-read book that allows busy administrators to master the fundamentals and practical skills of managing, securing, and automating IIS administrative tasks, even without prior experience. In just one hour a day, the reader can follow 24 bite-sized lessons filled with practice examples, high-value troubleshooting techniques, and step-by-step instructions.
The book delves into topics such as web farm design, website management, high availability, and securing servers, making it a valuable resource for both IT professionals and reluctant administrators.
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Authors: Jason Helmick
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Publisher: Simon and Schuster
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Published Date: December 31, 2013
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Page Count: 669
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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From Theory to Algorithms
Dive into the fascinating world of machine learning with the authors Shai Shalev-Shwartz and Shai Ben-David. This comprehensive guide, "Understanding Machine Learning: From Theory to Algorithms, " is a must-read for anyone seeking to grasp the fundamental concepts and applications of this rapidly evolving field. The book meticulously outlines the principles and methodologies that underpin automated learning approaches, providing readers with a deep understanding of their underlying considerations and potential implications.
With its accessible language and engaging content, "Understanding Machine Learning" deftly navigates the complexities of its subject matter, enabling you to confidently explore the realm of AI algorithms and technologies. A valuable addition to any computer science library, this 415-page book promises an enlightening journey through the algorithmic paradigms of machine learning. Whether you're a novice or an experienced researcher, prepare to be captivated by the captivating world of machine learning as you delve into its fascinating theory and practice. So, if you're ready to embark on a rewarding intellectual adventure, "Understanding Machine Learning" is your definitive guide.
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Authors: Shai Shalev-Shwartz, Shai Ben-David
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Publisher: Cambridge University Press
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Published Date: May 19, 2014
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Page Count: 415
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Print Type: BOOK
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Categories: Computers
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Average Rating: 5.0
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Ratings Count: 2.0
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Maturity Rating: NOT_MATURE
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Language: en
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In the ever-evolving world of artificial intelligence, "Introduction to Machine Learning" by Ethem Alpaydin is the ultimate guide for understanding the foundation of this transformative technology. Published by the esteemed MIT Press in 2014, this comprehensive book provides insights into the fascinating field of machine learning, designed for a wide range of readers—from beginners to seasoned professionals.
"Introduction to Machine Learning" begins with an introduction to the basics of supervised learning, taking the reader through a captivating journey of discovery. The text explores the intricacies of Bayesian decision theory and parametric methods, delving into multivariate techniques and dimensionality reduction with engaging clarity.
One of the book's strengths is its comprehensive coverage of clustering, nonparametric methods, decision trees, and linear discrimination. This enables readers to explore the complexities of multilayer perceptrons and the development of local models. The book then moves on to introduce kernel machines, graphical models, and hidden markov models, providing a comprehensive overview of the subject matter.
In the latter part of the book, Alpaydin delves into the realm of Bayesian estimation, reinforcing the importance of combining multiple learners to maximize efficiency. The final chapters of "Introduction to Machine Learning" explore the intricate field of reinforcement learning and the design and analysis of machine learning experiments.
Categorized under "Computers, " this book perfectly bridges the gap between theory and practicality, allowing readers to appreciate the vast potential of machine learning for various applications. Recommended for anyone interested in artificial intelligence or seeking to delve into the world of machine learning, this book is a must-read for both hobbyists and professionals alike.
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Authors: Ethem Alpaydin
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Publisher: MIT Press
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Published Date: August 22, 2014
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Page Count: 639
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Print Type: BOOK
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Categories: Computers
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Average Rating: 3.0
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Ratings Count: 2.0
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Maturity Rating: NOT_MATURE
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Language: en
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Unlock the secrets of predictive analytics with the essential guide to cutting-edge Python Machine Learning. This vital resource provides in-depth insights into Machine Learning, empowering you to uncover hidden patterns and structures in data and deliver powerful insights for your organization.
With Python Machine Learning, you'll learn how to harness the world's leading data science language and leverage its powerful open-source libraries to build robust statistical models. From deep learning and data visualization to neural networks and clustering, this comprehensive guide showcases the full range of Python's capabilities in machine learning.
Whether you're a seasoned data scientist or a newcomer to the field, Python Machine Learning offers unparalleled value and practical advice. You'll discover key strategies for optimizing and improving your machine learning systems and algorithms, while mastering data preprocessing techniques and unlocking the secrets of sentiment analysis.
Embark on the journey to success in today's challenging marketplace as you learn how to use Python to ask critical questions of your data and reveal hidden insights that can drive growth and innovation for your business. Whether you're a startup or a Fortune 500 organization, Python Machine Learning is an indispensable resource for anyone seeking to harness the power of machine learning and predictive analytics.
Delve into the fascinating world of data science with Python Machine Learning - it's time to ask better questions and let the answers guide you to new heights of success.
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Authors: Sebastian Raschka
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Publisher: Packt Publishing Ltd
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Published Date: September 23, 2015
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Page Count: 455
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Print Type: BOOK
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Categories: Computers
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Average Rating: 5.0
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Ratings Count: 2.0
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Maturity Rating: NOT_MATURE
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Language: en
Unlock the power of machine learning with this approachable guide for beginners! In "Machine Learning For Dummies, " expert authors John Paul Mueller and Luca Massaron help demystify the complex concept and make it accessible to everyone, even those without a mathematical background.
This engaging and practical book covers the essential topics needed to kickstart your machine learning journey. With a focus on practical applications and common use cases such as fraud detection, web search optimization, and spam filtering, you'll learn to work with powerful programming languages like Python and R, as well as popular tools like R Studio and Anaconda.
Perfect for those who want to understand the daily impact of machine learning and how to get started on implementing it, this beginner-friendly guide is a must-read for anyone looking to make sense of this crucial field.
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Authors: John Paul Mueller, Luca Massaron
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Publisher: John Wiley & Sons
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Published Date: May 31, 2016
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Page Count: 432
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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The New AI
Explore the New AI in a concise overview of Machine Learning, a cutting-edge field transforming our lives. In this accessible guide by expert Ethem Alpaydin, delve into the technology's evolution, learning algorithms, and practical applications.
From recommendation systems to autonomous cars, Machine Learning revolutionizes our interaction with technology. Alpaydin offers an engaging account of the past, present, and future of this dynamic field, ultimately highlighting its ethical and legal implications.
Discover the fascinating world of AI with this comprehensive 225-page book published by MIT Press.
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Authors: Ethem Alpaydin
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Publisher: MIT Press
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Published Date: October 07, 2016
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Page Count: 225
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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Deep Learning is a groundbreaking, comprehensive guide to the world of deep learning, as written by three of the most prominent pioneers in the field. This book introduces readers to the intricacies and potential of deep learning techniques, covering the mathematical and conceptual background, practical applications, and cutting-edge research perspectives in the field. With chapters on linear algebra, probability theory, information theory, and machine learning techniques, the authors expertly guide readers through the complexities of deep learning.
The book is not only valuable for academic readers but also practical for those interested in industry applications of deep learning. From natural language processing and speech recognition to computer vision and bioinformatics, readers will learn about deep learning technique usages, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodologies in these various domains.
In the spirit of Elon Musk, the book is an ideal starting point for undergraduate and graduate students looking to build a career in the field or software engineers planning to integrate deep learning into their products and platforms. This informative and engaging volume promises to provide readers with a thorough understanding of deep learning and its applications.
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Authors: Ian Goodfellow, Yoshua Bengio, Aaron Courville
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Publisher: MIT Press
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Published Date: November 10, 2016
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Page Count: 801
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Print Type: BOOK
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Categories: Computers
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Average Rating: 5.0
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Ratings Count: 1.0
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Maturity Rating: NOT_MATURE
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Language: en
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Transform your understanding of data science with 'Machine Learning Algorithms, ' a comprehensive guide that offers a strong foundation for entering the world of machine learning and data science. Filled with concept-rich, yet highly practical content, this book serves as a one-stop solution for learning everything that matters in mastering the implementation of various machine learning algorithms.
Designed for IT professionals looking to break into the field of data science, this book requires familiarity with languages such as R and Python. It offers in-depth insights into important elements of machine learning, feature selection and feature engineering processes, and how to manage performance and error trade-offs for linear regression.
This guide not only covers algorithms like linear regression, logistic regression, SVM, Naive Bayes, K-means, random forest, TensorFlow, and feature engineering but also delves into the concept of natural processing language and recommendation systems. With real-world examples and a step-by-step approach, 'Machine Learning Algorithms' makes it easy for you to understand and apply machine learning algorithms to solve complex data-driven problems.
By the end of this book, you will have gained the knowledge to select machine learning algorithms for clustering, classification, or regression tasks, ready to apply your newfound expertise in the dynamic world of data science.
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Authors: Giuseppe Bonaccorso
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Publisher: Packt Publishing Ltd
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Published Date: July 24, 2017
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Page Count: 360
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Print Type: BOOK
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Categories: Computers
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Average Rating: 4.5
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Ratings Count: 2.0
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Maturity Rating: NOT_MATURE
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Language: en
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"Advances in Financial Machine Learning" revolutionizes the investment world by blending financial expertise with cutting-edge machine learning techniques. Authored by acclaimed expert and portfolio manager, Marcos Lopez de Prado, this comprehensive guide takes readers on an unforgettable journey through the intricacies of implementing ML in finance.
This groundbreaking book demystifies the transformation occurring in today's financial landscape, illustrating how even the most complex tasks can now be efficiently tackled by these advanced algorithms. Readers will discover how to meticulously structure Big data for optimal use in ML algorithms, and explore groundbreaking research methods that pave the way for successful investment strategies.
Going beyond mere theory, "Advances in Financial Machine Learning" is meticulously crafted to address the practical problems faced by finance professionals on a daily basis. Each solution is founded on solid scientific principles, backed by mathematical rigor and supported by pertinent code examples.
By the end of the book, readers will be able to implement the proposed solutions within their specific financial contexts, making them active participants in this evolving field. "Advances in Financial Machine Learning" is more than just a book; it's a transformative tool that equips investment professionals with the game-changing technologies needed to thrive in the era of disruptive finance.
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Authors: Marcos Lopez de Prado
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Publisher: John Wiley & Sons
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Published Date: January 23, 2018
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Page Count: 400
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Print Type: BOOK
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Categories: Business & Economics
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Average Rating: 5.0
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Ratings Count: 1.0
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Maturity Rating: NOT_MATURE
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Language: en
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Discover the fascinating world of deep learning with Grokking Deep Learning: a comprehensive guide to neural network creation and training. Authored by Andrew W. Trask, a seasoned expert in deep learning, this engaging book offers an accessible and intuitive exploration of one of the most exciting and rapidly evolving branches of artificial intelligence.
As you embark on this journey, Trask illuminates the intricate science behind deep learning, empowering you to truly understand the complex mechanics of training neural networks. Employing only Python and the math-supporting library, NumPy, you'll learn the ins and outs of constructing your own neural networks, capable of image recognition, text translation, and even Shakespearean-style writing.
With Grokking Deep Learning, you'll gain a solid foundation in deep learning concepts, including neural prediction, gradient descent, regularization, and convolutional neural networks. Delve into the intricacies of probability and nonlinearity with activation functions such as sigmoids and ReLU. Furthermore, explore the vast potential of language models and recurrent layers to produce human-like text, paving the way for advanced natural language processing applications.
Trask's insightful approach to explaining deep learning concepts ensures that readers with a high school-level math and intermediate programming background can fully comprehend and appreciate the nuances of the field. As you progress through Grokking Deep Learning, you'll be well-equipped to tackle more advanced deep learning frameworks and continue exploring the vast frontier of artificial intelligence.
Purchase of the print version of Grokking Deep Learning comes with a free, comprehensive eBook edition, ensuring that you can access this valuable resource on your preferred device. Don't miss out on your chance to demystify the secrets of deep learning and unlock the potential of neural networks in our ever-evolving digital landscape!
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Authors: Andrew W. Trask
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Publisher: Simon and Schuster
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Published Date: January 23, 2019
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Page Count: 475
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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Implement Machine Learning Algorithms and Techniques to Build Intelligent Systems, 2nd Edition
Embark on a riveting adventure into the captivating world of machine learning with "Python Machine Learning by Example," a thorough guide that bridges the gap between theory and practice. In this newly revised version, author Yuxi (Hayden) Liu skillfully takes you through the intricate complexities of machine learning ideas and techniques using Python libraries such as TensorFlow and scikit-learn. With its straightforward, user-friendly approach and practical examples, you'll develop a deep-seated comprehension of data mining, data analytics, exploratory data analysis, feature engineering, and natural language processing.
Upon completion of this immersive expedition, you will have witnessed the power of Python in implementing data-driven solutions, with a formidable grasp of popular Python packages and tools like TensorFlow, scikit-learn, gensim, and Keras. The book also assists you in exploring popular machine learning algorithms through illuminating examples and familiarizes you with the best practices of employing these techniques in the present-day demanding data science domain.
Whether you are a machine learning aspirant, data analyst, or diligent data engineer, this book presents an inclusive and indispensable starting point to the world of machine learning, provided you possess preliminary knowledge of Python programming and basic understanding of statistical principles. "Python Machine Learning by Example" is a must-read for individuals eager to realize their full potential and yield significant outcomes in the thriving field of machine learning.
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Authors: Yuxi (Hayden) Liu
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Published Date: February 28, 2019
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Page Count: 382
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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Immerse yourself in the world of wetlands with Wetlands: Ecosystem Services, Restoration and Wise Use, a comprehensive book that delves into the major services these unique ecosystems provide. From climate cooling to water quality improvement, the book explores these critical functions and discusses recent conservation and restoration efforts in China and neighboring countries.
The authors also analyze the role of wetlands in global climate change, as they can initially contribute to warming but ultimately switch to cooling. Offering valuable insights into invasive species in coastal wetlands, the protection and wise use of tidal flats, and promising regional case studies on wetland restoration, this book is a must-read for academics, students, and professionals in the fields of wetland ecology, management, and restoration.
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Authors: Shuqing An, Jos T. A. Verhoeven
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Publisher: Springer
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Published Date: July 04, 2019
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Page Count: 322
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Print Type: BOOK
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Categories: Science
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Maturity Rating: NOT_MATURE
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Language: en
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Introducing Machine Learning: Master the foundations of AI and develop real-world solutions with Microsoft's powerful ML. NET library. Experienced authors Dino and Francesco Esposito guide readers through artificial intelligence basics and the machine learning pipeline.
Discover how to classify data, build neural networks, and utilize Azure cloud services for building AI applications faster. This comprehensive resource is perfect for professionals seeking to learn machine learning concepts and apply them to their projects, including data scientists and developers needing to enhance their skills.
Containing real-world examples built using the ML. NET library, this book offers practical knowledge and insights to take advantage of machine learning's vast potential.
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Authors: Dino Esposito, Francesco Esposito
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Publisher: Microsoft Press
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Published Date: January 31, 2020
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Page Count: 616
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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Unlock the power of machine learning and deep learning with Machine Learning and Deep Learning in Real-Time Applications. This cutting-edge book from IGI Global explores the practical and theoretical aspects of these intelligent agents and their ability to solve real-world problems in diverse professional disciplines.
Covering topics such as image processing, medical innovations, and smart grids, this comprehensive guide is perfect for researchers, academicians, scientists, industry experts, scholars, IT professionals, engineers, and students alike. With 344 captivating pages, this essential title provides current research on the multifaceted uses and implementations of machine learning and deep learning across the globe. Don't miss out on this must-read for anyone seeking to understand and harness the full potential of artificial intelligence in today's rapidly evolving professional landscape.
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Authors: Mahrishi, Mehul, Hiran, Kamal Kant, Meena, Gaurav, Sharma, Paawan
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Publisher: IGI Global
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Published Date: April 24, 2020
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Page Count: 344
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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Plunge into the captivating realm of explicable machine learning with Christoph Molnar's extensive guide. In "Explicable Machine Learning," you will acquire a robust comprehension of creating machine learning models and their choices understandable, through meticulously chosen elementary, comprehensible models and model-unbiased techniques.
Grasp essential interpretability principles, then venture into decision trees, decision rules, and linear regression. As the book advances, it delves into advanced model-unbiased methods, such as feature importance and accumulated local effects. Gain practical experience with Shapley values and LIME, elucidating individual forecasts and making them more comprehensible.
Comprehend how each interpretation technique functions, unearth their strengths and pitfalls, and become adept at selecting the most fitting approach for your machine learning project. Discover the potency of explicable machine learning to revolutionize your perspective on AI models and their decision-making. With its comprehensive clarifications and engaging prose, "Explicable Machine Learning" is the ideal companion for any data scientist eager to augment their model's transparency and utility.
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Authors: Christoph Molnar
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Publisher: Lulu.com
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Published Date: May 17, 2020
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Page Count: 320
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Print Type: BOOK
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Categories: Artificial intelligence
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Average Rating: 4.5
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Ratings Count: 2.0
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Maturity Rating: NOT_MATURE
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Language: en
Explore the fascinating realm of Artificial Intelligence and Machine Learning with Laurence Moroney's "AI and Machine Learning for Developers." This in-depth guide serves as an ideal starting point for those keen to transform from a developer to an AI specialist. Designed with a focus on pragmatic, code-based lessons, this book empowers you to build confidence while learning crucial concepts and scenarios.
The book offers a hands-on experience in machine learning, addressing computer vision, natural language processing, and sequence modeling for diverse runtime environments. It presents a beginner-friendly approach to advanced topics without overwhelming with intricate mathematical concepts.
With this guide, learn how to craft models using TensorFlow, a highly sought-after skill by employers. Gain foundational knowledge of machine learning through interactive coding examples. Master the art of computer vision, including identification of features in images, and implement natural language processing to sequence and tokenize words and sentences.
Uncover techniques for embedding models in Android and iOS, along with serving models over the web and cloud using TensorFlow Serving. This comprehensive 393-page exploration into AI and machine learning is a must-read for aspiring developers looking to elevate their programming capabilities.
No time to waste, embark on your AI and machine learning adventure today with "AI and Machine Learning for Developers," published by O'Reilly Media.
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Authors: Laurence Moroney
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Publisher: O'Reilly Media
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Published Date: October 01, 2020
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Page Count: 393
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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A Project-Based Introduction to Artificial Intelligence
Enter the world of artificial intelligence with "Machine Learning for Kids: A Project-Based Introduction to Artificial Intelligence"! This captivating guide, perfect for kids aged 12 and up, unveils the fascinating world of machine learning and AI through engaging, hands-on projects.
With the use of the Scratch programming language, young learners will embark on an exciting journey, where they'll create thrilling AI-powered games and applications. The book will demystify the concepts of machine learning and AI while making it fun and accessible for complete beginners.
Discover the wonders of AI as you progress through 13 hands-on projects, building an AI-based Rock, Paper, Scissors game, a movie recommendation app, and even a smart Pac-Man character! With its comprehensive tutorial for beginners and step-by-step instructions for each project, this book will ignite your child's passion for AI and coding. Grab "Machine Learning for Kids" and let the AI adventure begin!
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Authors: Dale Lane
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Publisher: No Starch Press
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Published Date: January 19, 2021
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Page Count: 290
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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Hyperparameter Optimization, Neural Architecture Search, and Algorithm Selection with Cloud Platforms
Dive into the world of automated machine learning (AutoML) and enhance your skills to build accurate, efficient, and user-friendly machine learning models. "Automated Machine Learning" provides a comprehensive exploration of AutoML and its various methodologies, with a focus on hyperparameter optimization, neural architecture search, and algorithm selection.
In this book, you will learn to implement AutoML using open-source tools and major cloud service providers like Azure, AWS, and GCP. As you progress, you'll discover how to automate time-consuming tasks in the machine learning process, increasing productivity and efficiency.
With this book, you will gain invaluable insights into the features and usage of cloud AutoML platforms. You'll also learn how to develop explainable AutoML pipelines, ensuring transparency and interoperability in your models. Additionally, the book covers automated feature engineering, time series forecasting, and data science modeling tasks to help you focus on more complex problems.
"Automated Machine Learning" is perfect for citizen data scientists, machine learning developers, AI enthusiasts, and anyone looking to build accurate and efficient machine learning models using open-source tools and cloud platforms. Basic knowledge of building ML models and experience with enterprise cloud is beneficial for getting the most out of this book.
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Authors: Adnan Masood
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Publisher: Packt Publishing
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Published Date: February 18, 2021
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Page Count: 312
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Print Type: BOOK
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Maturity Rating: NOT_MATURE
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Language: en
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"Machine Learning Algorithms for Data Scientists: An Overview" is your ultimate guide to the fascinating world of machine learning algorithms. Written by Vinaitheerthan Renganathan, this comprehensive book delves into the various applications of machine learning, showcasing its impact in sectors ranging from artificial intelligence to business. With a strong emphasis on the mathematical and statistical foundations that underpin these algorithms, this book is a must-read for anyone looking to gain a deeper understanding of machine learning concepts.
As you journey through this insightful text, you'll find that machine learning models are not confined to standard algorithms, but adapt and learn from the data over time. This process, known as model improvement, ensures optimal prediction and classification accuracy, making machine learning models indispensable tools in the modern data science landscape.
The book is designed to cater to data scientists of all levels, providing a short introduction to R, a prominent software tool used in machine learning. By assuming that readers have baseline skills in descriptive and inferential statistics, Renganathan ensures that his content remains accessible to a wide range of learners.
To facilitate a hands-on experience, the author has made the data sets used in the book available for download from his website. This resource enables readers to experiment with machine learning practices and gain practical experience in real-world settings.
With a concise 102 pages and a focus on the practical aspects of machine learning, "Machine Learning Algorithms for Data Scientists: An Overview" is a book that transcends theoretical concepts and delivers actionable insights. If you're ready to embark on a journey that uncovers the powerful potential of machine learning algorithms, this book is an essential companion.
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Authors: Vinaitheerthan Renganathan
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Publisher: Vinaitheerthan Renganathan
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Published Date: June 02, 2021
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Page Count: 102
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en
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Develop machine learning and deep learning models with Python
Embark on an enlightening journey in the world of machine learning and deep learning with "Machine Learning with PyTorch and Scikit-Learn" by Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili, and Dmytro Dzhulgakov. This essential guide, published by Packt Publishing Ltd, brings the best of scikit-learn and PyTorch frameworks to help you build advanced machine learning models with practical applications.
Whether you're a Python developer dipping your toes in machine learning for the first time or a data scientist wanting to master the latest developments, this comprehensive book covers all the essential concepts and techniques. With crystal-clear explanations, visualizations, and examples, it leaves no stone unturned in exploring the fundamentals of machine learning and deep learning, offering insights into the best practices and trends.
Key topics include applying machine learning principles with a strong theoretical foundation, understanding PyTorch framework, understanding transformers, XGBoost, graph neural networks, GANs for data generation, and reinforcement learning for intelligent agents. The book also delves into modern advancements such as graph neural networks and large-scale transformers employed in natural language processing (NLP).
"Machine Learning with PyTorch and Scikit-Learn" is your trusted companion as you traverse the landscape of machine learning and deep learning using Python. Whether you're coding your first machine learning classifier or seeking to expand your knowledge on cutting-edge approaches, this resource is the cornerstone of your learning journey.
Note: This book assumes a strong foundation in Python basics, calculus, and linear algebra, making it a suitable reference for anyone who is a Python practitioner or data scientist hoping to expand their machine learning knowledge.
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Authors: Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili, Dmytro Dzhulgakov
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Publisher: Packt Publishing Ltd
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Published Date: February 25, 2022
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Page Count: 775
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Print Type: BOOK
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Categories: Computers
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Maturity Rating: NOT_MATURE
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Language: en