All Indian Reprints of O'Reilly are printed in Grayscale
With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. 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.
Minimum quantity for "Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines (Greyscale Indian Edition)" is 1.
Turning text into valuable information is essential for businesses looking to gain a competitive advantage. With recent improvements in natural language processing (NLP), users now have many options for solving complex challenges. But it's not always clear which NLP tools or libraries would work for a business's needs, or which techniques you should use and in what order.
Minimum quantity for "Blueprints for Text Analysis Using Python: Machine Learning-Based Solutions for Common Real World (NLP) Applications" is 1.
This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout.
Minimum quantity for "Introducing MLOps: How to Scale Machine Learning in the Enterprise" is 1.
Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to achieve arbitrary goals. This exciting development avoids constraints found in traditional machine learning (ML) algorithms. This practical book shows data science and AI professionals how to learn by reinforcement and enable a machine to learn by itself.
Minimum quantity for "Reinforcement Learning:Industrial Applications of Intelligent Agents" is 1.
If you're training a machine learning model but aren't sure how to put it into production, this book will get you there. Kubeflow provides a collection of cloud native tools for different stages of a model's lifecycle, from data exploration, feature preparation, and model training to model serving. This guide helps data scientists build production-grade machine learning implementations with Kubeflow and shows data engineers how to make models scalable and reliable.
Using examples throughout the book, authors Holden Karau, Trevor Grant, Ilan Filonenko, Richard Liu, and Boris Lublinsky explain how to use Kubeflow to train and serve your machine learning models on top of Kubernetes in the cloud or in a development environment on-premises.
Minimum quantity for "Kubeflow for Machine Learning: From Lab to Production" is 1.
If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics.
You'll understand how to implement the most common scenarios in machine learning, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes. Most books on machine learning begin with a daunting amount of advanced math. This guide is built on practical lessons that let you work directly with the code.
Minimum quantity for "AI and Machine Learning for Coders: A Programmer's Guide to Artificial Intelligence" is 1.
Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP).
Minimum quantity for "Machine Learning and Data Science Blueprints for Finance: From Building Trading Strategies to Robo-Advisors Using Python" is 1.
The design patterns in this book capture best practices and solutions to recurring problems in machine learning. Authors Valliappa Lakshmanan, Sara Robinson, and Michael Munn catalog the first tried-and-proven methods to help engineers tackle problems that frequently crop up during the ML process. These design patterns codify the experience of hundreds of experts into advice you can easily follow.
Minimum quantity for "Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps" is 1.
Companies are spending billions on machine learning projects, but it's money wasted if the models can't be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You'll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems.
Minimum quantity for "Building Machine Learning Pipelines: Automating Model Life Cycles with TensorFlow" is 1.
Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you'll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers-including experienced practitioners and novices alike will learn the tools, best practices, and challenges involved in building a real-world ML application step by step.
Author Emmanuel Ameisen, an experienced data scientist who led an AI education program, demonstrates practical ML concepts using code snippets, illustrations, screenshots, and interviews with industry leaders. Part I teaches you how to plan an ML application and measure success. Part II explains how to build a working ML model. Part III demonstrates ways to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies.
This book will help you:
Minimum quantity for "Building Machine Learning Powered Applications: Going from Idea to Product" is 1.
Minimum quantity for "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, Second Edition (4-Colour Edition)" is 1.
Many industry experts
consider unsupervised learning the next frontier in artificial intelligence,
one that may hold the key to general artificial intelligence. Since the
majority of the world's data is unlabeled, conventional supervised learning
cannot be applied. Unsupervised learning, on the other hand, can be applied to
unlabeled datasets to discover meaningful patterns buried deep in the data,
patterns that may be near impossible for humans to uncover.
Minimum quantity for "Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabelled Data" is 1.