Machine learning real world (176 Ergebnisse)

- Softcover
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Paperback. Zustand: Good. DESCRIPTION In a world where big data is the norm and near-real-time decisions are crucial, machine learning (ML) is a critical component of the data workflow. Machine learning systems can quickly crunch massive amounts of information to offer insights and make decisions in a way that matches or even surpasses human cognitive abilities. These systems use sophisticated computational and statistical tools to build models that can recognize and visualize patterns, predict outcomes, forecast values, and make recommendations. Real-World Machine Learning is a practical guide designed to teach developers the art of ML project execution. The book introduces the day-to-day practice of machine learning and prepares readers to successfully build and deploy powerful ML systems. Using the Python language and the R statistical package, it starts with core concepts like data acquisition and modeling, classification, and regression. Then it moves through the most important ML tasks, like model validation, optimization and feature engineering. It uses real-world examples that help readers anticipate and overcome common pitfalls. Along the way, they will discover scalable and online algorithms for large and streaming data sets. Advanced readers will appreciate the in-depth discussion of enhanced ML systems through advanced data exploration and pre-processing methods. KEY FEATURES Accessible and practical introduction to machine learning Contains big-picture ideas and real-world examples Prepares reader to build and deploy powerful predictive systems Offers tips & tricks and highlights common pitfalls AUDIENCE Code examples are in Python and R. No prior machine learning experience required. ABOUT THE TECHNOLOGY Machine learning has gained prominence due to the overwhelming successes of Google, Microsoft, Amazon, LinkedIn, Facebook, and others in their use of ML. The Gartner report predicts that big data analytics will be a $25 billion market by 2017, and financial firms, marketing organizations, scientific facilities, and Silicon Valley startups are all demanding machine learning skills from their developers.…

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- Hardcover
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Hardback. Zustand: Good. How can you successfully deploy AI?When AI works, it's nothing short of brilliant, helping companies make or save tremendous amounts of money while delighting customers on an unprecedented scale. When it fails, the results can be devastating. Most AI models never make it out of testing, but those failures aren't random. This practical guide to deploying AI lays out a human-first, responsible approach that has seen more than three times the success rate when compared to the industry average.In Real World AI, Alyssa Simpson Rochwerger and Wilson Pang share dozens of AI stories from startups and global enterprises alike featuring personal experiences from people who have worked on global AI deployments that impact billions of people every day.AI for business doesn't have to be overwhelming. Real World AI uses plain language to walk you through an AI approach that you can feel confident about-for your business and for your customers.…

- Hardcover
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Hardback. Zustand: Very Good. How can you successfully deploy AI?When AI works, it's nothing short of brilliant, helping companies make or save tremendous amounts of money while delighting customers on an unprecedented scale. When it fails, the results can be devastating. Most AI models never make it out of testing, but those failures aren't random. This practical guide to deploying AI lays out a human-first, responsible approach that has seen more than three times the success rate when compared to the industry average.In Real World AI, Alyssa Simpson Rochwerger and Wilson Pang share dozens of AI stories from startups and global enterprises alike featuring personal experiences from people who have worked on global AI deployments that impact billions of people every day.AI for business doesn't have to be overwhelming. Real World AI uses plain language to walk you through an AI approach that you can feel confident about-for your business and for your customers.…

- Hardcover
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- Softcover
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Paperback. Zustand: Very Good. A deep and detailed dive into the key aspects and challenges of machine learning interpretability, complete with the know-how on how to overcome and leverage them to build fairer, safer, and more reliable models Key Features Learn how to extract easy-to-understand insights from any machine learning model Become well-versed with interpretability techniques to build fairer, safer, and more reliable models Mitigate risks in AI systems before they have broader implications by learning how to debug black-box models Book DescriptionDo you want to gain a deeper understanding of your models and better mitigate poor prediction risks associated with machine learning interpretation? If so, then Interpretable Machine Learning with Python deserves a place on your bookshelf. We?ll be starting off with the fundamentals of interpretability, its relevance in business, and exploring its key aspects and challenges. As you progress through the chapters, you'll then focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. You?ll also get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, this book will also help you interpret model outcomes using examples. You?ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you?ll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining. By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning. What you will learn Recognize the importance of interpretability in business Study models that are intrinsically interpretable such as linear models, decision trees, and Naïve Bayes Become well-versed in interpreting models with model-agnostic methods Visualize how an image classifier works and what it learns Understand how to mitigate the influence of bias in datasets Discover how to make models more reliable with adversarial robustness Use monotonic constraints to make fairer and safer models Who this book is forThis book is primarily written for data scientists, machine learning developers, and data stewards who find themselves under increasing pressures to explain the workings of AI systems, their impacts on decision making, and how they identify and manage bias. It?s also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a solid grasp on the Python programming language and ML fundamentals is needed to follow along.…

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Python Machine Learning By Example: Unlock machine learning best practices with real-world use cases
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Sprache: Englisch
Verlag: Book Bound Studios, 2024
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