Machine Learning (Over 68,000 results)

Condition: Very Good. Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries. About This Book ? Second edition of the bestselling book on Machine Learning ? A practical approach to key frameworks in data science, machine learning, and deep learning ? Use the most powerful Python libraries to implement machine learning and deep learning ? Get to know the best practices to improve and optimize your machine learning systems and algorithms Who This Book Is For If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential and unmissable resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for developers and data scientists who want to teach computers how to learn from data. What You Will Learn ? Understand the key frameworks in data science, machine learning, and deep learning ? Harness the power of the latest Python open source libraries in machine learning ? Explore machine learning techniques using challenging real-world data ? Master deep neural network implementation using the TensorFlow library ? Learn the mechanics of classification algorithms to implement the best tool for the job ? Predict continuous target outcomes using regression analysis ? Uncover hidden patterns and structures in data with clustering ? Delve deeper into textual and social media data using sentiment analysis In Detail Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka's bestselling book, Python Machine Learning. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis. Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library. Sebastian Raschka and Vahid Mirjalili's unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you'll be ready to meet the new data analysis opportunities in today's world. If you've read the first edition of this book, you'll be delighted to find a new balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You'll be able to learn and work with TensorFlow more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn. Style and Approach Python Machine Learning Second Edition takes a practical, hands-on coding approach so you can learn about machine learning by coding with Python. This book moves fluently between the theoretical principles of machine learning and the practical details of implementation with Python.…

Paperback. Condition: Very Good. A concise overview of machine learning--computer programs that learn from data--which underlies applications that include recommendation systems, face recognition, and driverless cars.Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition--as well as some we don't yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data; the idea is to learn the algorithms for the tasks automatically from data. As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as Big Data has gotten bigger, the theory of machine learning--the foundation of efforts to process that data into knowledge--has also advanced. In this book, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications.Alpaydin offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today's machine learning boom in context. He describes the basics of machine learning and some applications; the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances, with such applications as customer segmentation and learning recommendations; and reinforcement learning, when an autonomous agent learns act so as to maximize reward and minimize penalty. Alpaydin then considers some future directions for machine learning and the new field of data science, and discusses the ethical and legal implications for data privacy and security.…

Paperback. Condition: Good. A concise overview of machine learning--computer programs that learn from data--which underlies applications that include recommendation systems, face recognition, and driverless cars.Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition--as well as some we don't yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data; the idea is to learn the algorithms for the tasks automatically from data. As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as Big Data has gotten bigger, the theory of machine learning--the foundation of efforts to process that data into knowledge--has also advanced. In this book, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications.Alpaydin offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today's machine learning boom in context. He describes the basics of machine learning and some applications; the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances, with such applications as customer segmentation and learning recommendations; and reinforcement learning, when an autonomous agent learns act so as to maximize reward and minimize penalty. Alpaydin then considers some future directions for machine learning and the new field of data science, and discusses the ethical and legal implications for data privacy and security.…

Paperback. Condition: Fair. 1. The item might be beaten up but readable. May contain markings or highlighting, as well as stains, bent corners, or any other major defect, but the text is not obscured in any way.

Condition: good. Gently used with minimal wear on the corners and cover. A few pages may contain light highlighting or writing, but the text remains fully legible. Dust jacket may be missing, and supplemental materials like CDs or codes may not be included. May be ex-library with library markings. Ships promptly. …

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Paperback. Condition: Very Good. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance.

Paperback. Condition: Fair. Dig deep into the data with a hands-on guide to machine learning Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals.…

Paperback. Condition: Good. Dig deep into the data with a hands-on guide to machine learning Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals.…

Condition: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc.

Condition: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc.

Condition: very_good. Book is in very good condition and may include minimal underlining highlighting. The book can also include "From the library of" labels. May not contain miscellaneous items toys, dvds, etc. . We offer 100% money back guarantee and 24 7 customer service.

Condition: very_good. Book is in very good condition and may include minimal underlining highlighting. The book can also include "From the library of" labels. May not contain miscellaneous items toys, dvds, etc. . We offer 100% money back guarantee and 24 7 customer service.

Condition: very_good. Book is in very good condition and may include minimal underlining highlighting. The book can also include "From the library of" labels. May not contain miscellaneous items toys, dvds, etc. . We offer 100% money back guarantee and 24 7 customer service.

Condition: good. Book is in good condition and may include underlining highlighting and minimal wear. The book can also include "From the library of" labels. May not contain miscellaneous items toys, dvds, etc. . We offer 100% money back guarantee and 24 7 customer service.

Condition: very_good. Book is in very good condition and may include minimal underlining highlighting. The book can also include "From the library of" labels. May not contain miscellaneous items toys, dvds, etc. . We offer 100% money back guarantee and 24 7 customer service.

Condition: very_good. Book is in very good condition and may include minimal underlining highlighting. The book can also include "From the library of" labels. May not contain miscellaneous items toys, dvds, etc. . We offer 100% money back guarantee and 24 7 customer service.

Condition: very_good. Book is in very good condition and may include minimal underlining highlighting. The book can also include "From the library of" labels. May not contain miscellaneous items toys, dvds, etc. . We offer 100% money back guarantee and 24 7 customer service.

paperback. Condition: Good.

paperback. Condition: Good. Good clean copy. Minimal signs of wear.

Paperback. Condition: Fair. Learn a simpler and more effective way to analyze data and predict outcomes with Python Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python.

Paperback. Condition: Good. Put the power of AWS Cloud machine learning services to work in your business and commercial applications!  Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabilities of the Amazon Web Services ecosystem and provides practical examples to solve real-world regression and classification problems. While readers do not need prior ML experience, they are expected to have some knowledge of Python and a basic knowledge of Amazon Web Services. Part One introduces readers to fundamental machine learning concepts. You will learn about the types of ML systems, how they are used, and challenges you may face with ML solutions. Part Two focuses on machine learning services provided by Amazon Web Services. You?ll be introduced to the basics of cloud computing and AWS offerings in the cloud-based machine learning space. Then you?ll learn to use Amazon Machine Learning to solve a simpler class of machine learning problems, and Amazon SageMaker to solve more complex problems. ?    Learn techniques that allow you to preprocess data, basic feature engineering, visualizing data, and model building ?    Discover common neural network frameworks with Amazon SageMaker ?    Solve computer vision problems with Amazon Rekognition ?    Benefit from illustrations, source code examples, and sidebars in each chapter The book appeals to both Python developers and technical/solution architects. Developers will find concrete examples that show them how to perform common ML tasks with Python on AWS. Technical/solution architects will find useful information on the machine learning capabilities of the AWS ecosystem.…

Condition: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc.

Paperback. Condition: Good. As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.…

Condition: acceptable. This copy has clearly been enjoyedâ"expect noticeable shelf wear and some minor creases to the cover. Binding is strong, and all pages are legible. May contain previous library markings or stamps.

paperback. Condition: Good.

Condition: good. Gently used with minimal wear on the corners and cover. A few pages may contain light highlighting or writing, but the text remains fully legible. Dust jacket may be missing, and supplemental materials like CDs or codes may not be included. May be ex-library with library markings. Ships promptly. …

Paperback. Condition: Good. This fully revised second edition of Machine Learning with TensorFlow teaches you the foundational concepts of machine learning and how to utilize the TensorFlow library to rapidly build powerful ML models. You?ll learn the basics of regression, classification, and clustering algorithms, applying them to solve real-world challenges.   New and revised content expands coverage of core machine learning algorithms and advancements in neural networks such as VGG-Face facial identification classifiers and deep speech classifiers. Written by NASA JPL Deputy CTO and Principal Data Scientist Chris Mattmann, all examples are accompanied by downloadable Jupyter Notebooks for a hands-on experience coding TensorFlow with Python.   Key Features ·   Visualizing algorithms with TensorBoard ·   Understanding and using neural networks ·   Reproducing and employing predictive science ·   Downloadable Jupyter Notebooks for all examples ·   Questions to test your knowledge ·   Examples use the super-stable 1.14.1 branch of TensorFlow   Developers experienced with Python and algebraic concepts like vectors and matrices.   About the technology TensorFlow, Google?s library for large-scale machine learning, makes powerful ML techniques easily accessible. It simplifies often-complex computations by representing them as graphs that are mapped to machines in a cluster or to the processors of a single machine. Offering a complete ecosystem for all stages and types of machine learning, TensorFlow?s end-to-end functionality empowers machine learning engineers of all skill levels to solve their problems with ML.   Chris Mattmann is the Deputy Chief Technology and Innovation Officer at NASA Jet Propulsion Lab, where he has been recognised as JPL's first Principal Scientist in the area of Data Science. Chris has applied TensorFlow to challenges he?s faced at NASA, including building an implementation of Google?s Show & Tell algorithm for image captioning using TensorFlow. He contributes to open source as a former Director at the Apache Software Foundation, and teaches graduate courses at USC in Content Detection and Analysis, and in Search Engines and Information Retrieval.   Nishant Shukla wrote the first edition of Machine Learning with TensorFlow.…