Machine Learning for Financial Risk Management with Python

With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for ad

Machine Learning with Spark and Python Essential Techniques for Predictive Analytics Second Edition simplifies ML for practical uses by focusing on two key algorithms. This new second edition improves

This book covers machine learning, one of the hottest topics in more recent years. With computing power increasing exponentially and costs decreasing at the same time, there is no better time for mach

Are you stuck in getting started with machine learning with python? A Step-By-Step Guide to Learn and Master Python Machine Learning walks you through steps for getting started with Machine Learning w

Business analysts, managers, researchers, and students are rushing to master powerful machine learning techniques for improving decision-making and scaling analysis to immense datasets. Machine Learni

The Python programming language and its libraries, including pandas and scikit-learn, provide a production-grade environment to help you accomplish a broad range of machine-learning tasks. With this c

Gain a broad foundation of advanced data analytics concepts and discover the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB. This book discusses how to implement ETL techniqu

Build your own chatbot using Python and open source tools. This book begins with an introduction to chatbots where you will gain vital information on their architecture. You will then dive straight in

Learn to harness the power of AI for natural language processing, performing tasks such as spell check, text summarization, document classification, and natural language generation. Along the way, you

Financial risk management is a growing field of specialization in business. With the increased level of regulation and emphasis on financial reporting, the role of the financial risk manager has neve

Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You’ll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You’ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you’ll explore how models are made in real time and then deployed using various DevOps tools. All the concepts in Practical Machine Learning and Image Processing are explained using r

Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies from each of these domains: you’ll see examples that demonst

Understand critical cybersecurity and risk perspectives, insights, and tools for the leaders of complex financial systems and markets. This book offers guidance for decision makers and helps establish

A metaheuristic is a higher-level procedure designed to select a heuristic (partial search algorithm) that may lead to a sufficiently good solution to an optimization problem, especially with incomple

The theory and practice of reducing the environmental risk of chemicals is a key issue in environmental protection. This volume deals with remediation technologies and introduces their functional clas

Safety Risk Management for Medical Devices demystifies risk management, providing clarity of thought and confidence to the practitioners of risk management as they do their work. Written with practici

Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory ne

A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework.Today many information source

Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosyste

Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic

Many industry experts consider unsupervised learning the next AI frontier, one that may hold the key to general artificial intelligence. Armed with the conceptual knowledge in this book, data scientis

Essentials of Financial Management is paperback edition of an Open Access e-textbook suitable for students with limited knowledge of finance and financial markets. It answers the main questions of a c

Empowering you for success, Brigham/Ehrhardt's FINANCIAL MANAGEMENT: THEORY AND PRACTICE, 16th edition, equips you with a solid understanding of key theoretical concepts along with practical tools to

This book bridges theoretical computer science and machine learning by exploring what the two sides can teach each other. It emphasizes the need for flexible, tractable models that better capture not what makes machine learning hard, but what makes it easy. Theoretical computer scientists will be introduced to important models in machine learning and to the main questions within the field. Machine learning researchers will be introduced to cutting-edge research in an accessible format, and gain familiarity with a modern, algorithmic toolkit, including the method of moments, tensor decompositions and convex programming relaxations. The treatment beyond worst-case analysis is to build a rigorous understanding about the approaches used in practice and to facilitate the discovery of exciting, new ways to solve important long-standing problems.

This book bridges theoretical computer science and machine learning by exploring what the two sides can teach each other. It emphasizes the need for flexible, tractable models that better capture not what makes machine learning hard, but what makes it easy. Theoretical computer scientists will be introduced to important models in machine learning and to the main questions within the field. Machine learning researchers will be introduced to cutting-edge research in an accessible format, and gain familiarity with a modern, algorithmic toolkit, including the method of moments, tensor decompositions and convex programming relaxations. The treatment beyond worst-case analysis is to build a rigorous understanding about the approaches used in practice and to facilitate the discovery of exciting, new ways to solve important long-standing problems.

The fourth edition of Essentials of Financial Management continues to provide students with a focused understanding of today’s corporate finance and financial management. This market-leading text offe

Disaster risk management is of increasing significance in today’s world. Every year, natural disasters cause tens of thousands of deaths and tens of billions of dollars’ worth of losses. Northeast Asi

Essential tools and guidance for effective nonprofit financial management Financial Management for Nonprofit Organizations provides students, professionals, and board members with a comprehensive refe

NOTE: Before purchasing, check with your instructor to ensure you select the correct ISBN. Several versions of the MyLab™and Mastering™ platforms exist for each title, and registrations ar