Applications modeling machine learning (90 Ergebnisse)

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Taschenbuch. Zustand: Neu. Neuware - Have you ever wanted to build intelligent systems but felt intimidated by the complexities of machine learning Do you dream of creating algorithms that can learn from data, make predictions, and solve real-world problems The future is now, and it's powered by machine learning. But you don't need a PhD to get started. All you need is the right guide.This isn't just another textbook. Machine Learning Algorithms with Python is your personal roadmap to mastering the most essential and powerful algorithms in the field. We cut through the jargon and get straight to the point, showing you exactly how to implement these algorithms using Python, the world's most popular language for data science.Imagine building a system that can accurately predict stock prices, a model that can identify fraudulent transactions in real-time, or an application that can categorize images with a high degree of precision. These aren't futuristic fantasies; they're the tangible results of mastering machine learning. This book provides you with the practical knowledge and hands-on examples to make these dreams a reality.We've structured this book to be both comprehensive and accessible. We start with the foundational concepts, explaining the 'why' before we show you the 'how.' Then, we dive into the core algorithms, from the simplicity of Linear Regression to the power of Neural Networks. You'll explore: - Supervised Learning: Classification and regression models like Support Vector Machines (SVMs), Decision Trees, and K-Nearest Neighbors (KNNs).- Unsupervised Learning: Discovering hidden patterns with K-Means Clustering and Principal Component Analysis (PCA).- Advanced Topics: Get a taste of Deep Learning and Reinforcement Learning to expand your capabilities even further.Every chapter is a project. You won't just be reading; you'll be doing. Each algorithm comes with a clear explanation, a complete Python implementation, and a real-world dataset to apply it to. By the time you finish this book, you won't just know about machine learning; you'll be able to build and deploy your own models with confidence.This book is perfect for programmers who want to transition into data science, students looking for a practical guide to complement their theoretical studies, and anyone with a curious mind ready to build the future. You have the potential to create something incredible. Stop waiting for someone else to build the future-it's time for you to build it yourself.The future of technology is being written in code. Are you ready to be one of the authors Get your copy of Machine Learning Algorithms with Python today and transform your career. The world is waiting for your innovation.…

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Buch. Zustand: Neu. Neuware - A wide-ranging overview of the use of machine learning and AI techniques in financial risk management, including practical advice for implementationRisk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning introduces readers to the use of innovative AI technologies for forecasting and evaluating financial risks. Providing up-to-date coverage of the practical application of current modelling techniques in risk management, this real-world guide also explores new opportunities and challenges associated with implementing machine learning and artificial intelligence (AI) into the risk management process.Authors Terisa Roberts and Stephen Tonna provide readers with a clear understanding about the strengths and weaknesses of machine learning and AI while explaining how they can be applied to both everyday risk management problems and to evaluate the financial impact of extreme events such as global pandemics and changes in climate. Throughout the text, the authors clarify misconceptions about the use of machine learning and AI techniques using clear explanations while offering step-by-step advice for implementing the technologies into an organization's risk management model governance framework. This authoritative volume:\* Highlights the use of machine learning and AI in identifying procedures for avoiding or minimizing financial risk\* Discusses practical tools for assessing bias and interpretability of resultant models developed with machine learning algorithms and techniques\* Covers the basic principles and nuances of feature engineering and common machine learning algorithms\* Illustrates how risk modeling is incorporating machine learning and AI techniques to rapidly consume complex data and address current gaps in the end-to-end modelling lifecycle\* Explains how proprietary software and open-source languages can be combined to deliver the best of both worlds: for risk models and risk practitionersRisk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning is an invaluable guide for CEOs, CROs, CFOs, risk managers, business managers, and other professionals working in risk management.…

Taschenbuch. Zustand: Neu. Neuware - Modeling Intelligence Systems: Build a Strong Mathematical Foundation for Artificial Intelligence, Machine Learning, Deep Learning, and Modern AI Applications Have you ever wondered what truly powers ChatGPT, autonomous vehicles, recommendation systems, facial recognition, or modern AI assistants Behind every intelligent system lies mathematics. If you've tried learning AI before and felt overwhelmed by complex equations or disconnected theory, you're not alone. The good news is that understanding the mathematics behind AI doesn't have to be intimidating-it simply requires the right guide. Modeling Intelligence Systems is a practical, beginner-friendly guide that explains the mathematical principles behind Artificial Intelligence, Machine Learning, and Deep Learning using clear language, intuitive explanations, and real-world examples. Rather than asking you to memorize formulas, this book helps you understand why they matter and how they are used to build intelligent systems. Whether you're a student, software developer, data scientist, AI enthusiast, or working professional, you'll develop the confidence to understand modern AI from the ground up. Inside this book, you'll learn the essential mathematics that powers today's AI systems, including linear algebra, calculus, probability, statistics, optimization, feature engineering, neural networks, deep learning, transformers, large language models, computer vision, reinforcement learning, recommendation systems, AI agents, model deployment, responsible AI, and the future of intelligent systems. Each topic is explained with practical examples, mathematical intuition, working code samples where appropriate, and exercises that reinforce your understanding. By the time you finish this book, you'll be able to read AI literature with greater confidence, understand how modern algorithms learn from data, make better technical decisions, and build a strong foundation for advanced topics in machine learning and artificial intelligence. Instead of relying solely on libraries and frameworks, you'll understand the mathematical ideas that make them work. Artificial intelligence is transforming software development, healthcare, finance, cybersecurity, education, manufacturing, robotics, and countless other industries. The professionals who understand both the theory and the practical application of AI will be best positioned to take advantage of these opportunities. Building that foundation today will prepare you for the technologies shaping tomorrow. If you're ready to move beyond using AI tools and start understanding the mathematics that powers them, scroll up and click 'Buy Now' to begin building a solid foundation in artificial intelligence today.…

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