Data science machine learning (Plus de 3 500 résultats)

Paperback. Etat : Very Good. Solve challenging data science problems by mastering cutting-edge machine learning techniques in Python About This Book ? Resolve complex machine learning problems and explore deep learning ? Learn to use Python code for implementing a range of machine learning algorithms and techniques ? A practical tutorial that tackles real-world computing problems through a rigorous and effective approach Who This Book Is For This title is for Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. If you've ever considered building your own image or text-tagging solution, or of entering a Kaggle contest for instance, this book is for you! Prior experience of Python and grounding in some of the core concepts of machine learning would be helpful. What You Will Learn ? Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms ? Apply your new found skills to solve real problems, through clearly-explained code for every technique and test ? Automate large sets of complex data and overcome time-consuming practical challenges ? Improve the accuracy of models and your existing input data using powerful feature engineering techniques ? Use multiple learning techniques together to improve the consistency of results ? Understand the hidden structure of datasets using a range of unsupervised techniques ? Gain insight into how the experts solve challenging data problems with an effective, iterative, and validation-focused approach ? Improve the effectiveness of your deep learning models further by using powerful ensembling techniques to strap multiple models together In Detail Designed to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques, semi-supervised learning, and more - all whilst working with real-world applications that include image, music, text, and financial data. The machine learning techniques covered in this book are at the forefront of commercial practice. They are applicable now for the first time in contexts such as image recognition, NLP and web search, computational creativity, and commercial/financial data modeling. Deep Learning algorithms and ensembles of models are in use by data scientists at top tech and digital companies, but the skills needed to apply them successfully, while in high demand, are still scarce. This book is designed to take the reader on a guided tour of the most relevant and powerful machine learning techniques. Clear descriptions of how techniques work and detailed code examples demonstrate deep learning techniques, semi-supervised learning and more, in real world applications. We will also learn about NumPy and Theano. By this end of this book, you will learn a set of advanced Machine Learning techniques and acquire a broad set of powerful skills in the area of feature selection & feature engineering. Style and approach This book focuses on clarifying the theory and code behind complex algorithms to make them practical, useable, and well-understood. Each topic is described with real-world applications, providing both broad contextual coverage and detailed guidance.…

Etat : good. Books may contain some notes and highlighting. Supplements such as Access Codes, Cd's Dust Jackets, etc. are not guaranteed with used book purchases.

Paperback. Etat : 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. Etat : 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.

Etat : 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.

Etat : 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. Etat : 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.…

Etat : 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.

Etat : 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. Etat : Good.

Etat : 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. Etat : Very Good. Solve challenging data science problems by mastering cutting-edge machine learning techniques in Python About This Book ? Resolve complex machine learning problems and explore deep learning ? Learn to use Python code for implementing a range of machine learning algorithms and techniques ? A practical tutorial that tackles real-world computing problems through a rigorous and effective approach Who This Book Is For This title is for Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. If you've ever considered building your own image or text-tagging solution, or of entering a Kaggle contest for instance, this book is for you! Prior experience of Python and grounding in some of the core concepts of machine learning would be helpful. What You Will Learn ? Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms ? Apply your new found skills to solve real problems, through clearly-explained code for every technique and test ? Automate large sets of complex data and overcome time-consuming practical challenges ? Improve the accuracy of models and your existing input data using powerful feature engineering techniques ? Use multiple learning techniques together to improve the consistency of results ? Understand the hidden structure of datasets using a range of unsupervised techniques ? Gain insight into how the experts solve challenging data problems with an effective, iterative, and validation-focused approach ? Improve the effectiveness of your deep learning models further by using powerful ensembling techniques to strap multiple models together In Detail Designed to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques, semi-supervised learning, and more - all whilst working with real-world applications that include image, music, text, and financial data. The machine learning techniques covered in this book are at the forefront of commercial practice. They are applicable now for the first time in contexts such as image recognition, NLP and web search, computational creativity, and commercial/financial data modeling. Deep Learning algorithms and ensembles of models are in use by data scientists at top tech and digital companies, but the skills needed to apply them successfully, while in high demand, are still scarce. This book is designed to take the reader on a guided tour of the most relevant and powerful machine learning techniques. Clear descriptions of how techniques work and detailed code examples demonstrate deep learning techniques, semi-supervised learning and more, in real world applications. We will also learn about NumPy and Theano. By this end of this book, you will learn a set of advanced Machine Learning techniques and acquire a broad set of powerful skills in the area of feature selection & feature engineering. Style and approach This book focuses on clarifying the theory and code behind complex algorithms to make them practical, useable, and well-understood. Each topic is described with real-world applications, providing both broad contextual coverage and detailed guidance.…

Paperback. Etat : Good. BigData and Machine Learning in Python and Spark.

Etat : good. Book shows general signs of use and handling. May have light wear on the cover or edges and minimal writing or highlighting. Binding remains tight, and pages are clean and readable.

Hardback. Etat : Good. Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product.Use machine learning to understand your customers, frame decisions, and drive value  The business analytics world has changed, and Data Scientists are taking over. Business Data Science takes you through the steps of using machine learning to implement best-in-class business data science.  Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you?ll find the information, insight, and tools you need to flourish in today?s data-driven economy. You?ll learn how to: ? Use the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling? Understand how use ML tools in real world business problems, where causation matters more that correlation? Solve data science programs by scripting in the R programming languageToday?s business landscape is driven by data and constantly shifting. Companies live and die on their ability to make and implement the right decisions quickly and effectively. Business Data Science is about doing data science right. It?s about the exciting things being done around Big Data to run a flourishing business. It?s about the precepts, principals, and best practices that you need know for best-in-class business data science.…

Paperback. Etat : 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. Etat : 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. Etat : Good. Python Machine Learning for BeginnersMachine Learning (ML) and Artificial Intelligence (AI) are here to stay. Yes, that's right. Based on a significant amount of data and evidence, it's obvious that ML and AI are here to stay.Consider any industry today. The practical applications of ML are really driving business results. Whether it's healthcare, e-commerce, government, transportation, social media sites, financial services, manufacturing, oil and gas, marketing and salesYou name it. The list goes on. There's no doubt that ML is going to play a decisive role in every domain in the future.But what does a Machine Learning professional do?A Machine Learning specialist develops intelligent algorithms that learn from data and also adapt to the data quickly. Then, these high-end algorithms make accurate predictions.Python Machine Learning for Beginners presents you with a hands-on approach to learn ML fast.How Is This Book Different?AI Publishing strongly believes in learning by doing methodology. With this in mind, we have crafted this book with care. You will find that the emphasis on the theoretical aspects of machine learning is equal to the emphasis on the practical aspects of the subject matter.You'll learn about data analysis and visualization in great detail in the first half of the book. Then, in the second half, you'll learn about machine learning and statistical models for data science.Each chapter presents you with the theoretical framework behind the different data science and machine learning techniques, and practical examples illustrate the working of these techniques.When you buy this book, your learning journey becomes so much easier. The reason is you get instant access to all the related learning material presented with this book-references, PDFs, Python codes, and exercises-on the publisher's website. All this material is available to you at no extra cost. You can download the ML datasets used in this book at runtime, or you can access them via the Resources/Datasets folder.You'll also find the short course on Python programming in the second chapter immensely useful, especially if you are new to Python. Since this book gives you access to all the Python codes and datasets, you only need access to a computer with the internet to get started. The topics covered include: Introduction and Environment SetupPython Crash CoursePython NumPy Library for Data AnalysisIntroduction to Pandas Library for Data AnalysisData Visualization via Matplotlib, Seaborn, and Pandas LibrariesSolving Regression Problems in ML Using Sklearn LibrarySolving Classification Problems in ML Using Sklearn LibraryData Clustering with ML Using Sklearn LibraryDeep Learning with Python TensorFlow 2.0Dimensionality Reduction with PCA and LDA Using SklearnClick the BUY NOW button to start your Machine Learning journey.…

Paperback. Etat : 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.…

Paperback. Etat : Very Good. Prepares students for Microsoft Exam 70-774and helps them demonstrate real-world mastery of performing key data science activities with Azure Machine Learning services. Designed for experienced IT students ready to advance their status, Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the MCSA level.…

paperback. Etat : Good.

Etat : good. USED book in GOOD condition. Great binding, pages and cover show normal signs of wear from use.

paperback. Etat : Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority.

paperback. Etat : Very Good. 1st Edition. Used - Very Good.

Etat : Good. Good condition. A copy that has been read but remains intact. May contain markings such as bookplates, stamps, limited notes and highlighting, or a few light stains.

Paperback. Etat : Fair. No Jacket. Readable copy. Pages may have considerable notes/highlighting. ~ ThriftBooks: Read More, Spend Less.

Paperback. Etat : As New. No Jacket. Pages are clean and are not marred by notes or folds of any kind. ~ ThriftBooks: Read More, Spend Less.

Paperback. Etat : Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.

Paperback. Etat : Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

Paperback. Etat : Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.