Machine Learning for Financial Risk Management with Python

Quickly scale up to Quantum computing and Quantum machine learning foundations and related mathematics and expose them to different use cases that can be solved through Quantum based algorithms.This b

Learn to master basic programming tasks from scratch with real-life, scientifically relevant examples and solutions drawn from both science and engineering. Students and researchers at all levels are increasingly turning to the powerful Python programming language as an alternative to commercial packages and this fast-paced introduction moves from the basics to advanced concepts in one complete volume, enabling readers to gain proficiency quickly. Beginning with general programming concepts such as loops and functions within the core Python 3 language, and moving on to the NumPy, SciPy and Matplotlib libraries for numerical programming and data visualization, this textbook also discusses the use of Jupyter Notebooks to build rich-media, shareable documents for scientific analysis. The second edition features a new chapter on data analysis with the pandas library and comprehensive updates, and new exercises and examples. A final chapter introduces more advanced topics such as floating-p

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every cha

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

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

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

Embrace machine learning approaches and Python to enable automatic rendering of rich insights and solve business problems. The book uses a hands-on case study-based approach to crack real-world applic

The programming landscape of natural language processing has changed dramatically in the past few years. Machine learning approaches now require mature tools like Python’s scikit-learn to apply

This book explores the accounting and regulatory framework associated with the acquisition and disposal of financial instruments; how to determine their value; how to manage the risk connected with th

This book examines the role of risk management in the recent financial crisis and applies lessons from there to the national security realm. It rethinks the way risk contributes to strategy, with insi

An effective guide to geographic information systems and remote sensing analysis using Python 3About This BookConstruct applications for GIS development by exploiting PythonThis focuses on built-in Py

Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning