Bloody Heart by Sophie Lark USA
Bloody Heart by Sophie Lark Original price was: $13.29.Current price is: $10.28.
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Savage Lover by Sophie Lark USA
Savage Lover by Sophie Lark Original price was: $9.49.Current price is: $6.48.

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The Broken Protector by Nicole Snow

Original price was: $13.28.Current price is: $10.27.

Binding: Paperback
Language: English
ISBN-13: 979-8399173085
Reader’s Age: Adults 18+
Ships Within: 5–10 Business Days
Author: Nicole Snow
Release Date: June 20, 2023
Genre: Romantic Suspense

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Description

The Broken Protector: A Small Town Enemies to Lovers Romance by Nicole Snow (Dark Hearts of Redhaven Series, Book 1)

About the Book

The Broken Protector is a steamy, heart-racing small-town romance packed with sharp wit, slow-burn tension, real danger, and a love story that hits you right in the chest. It is the perfect read for anyone who loves a grumpy-but-good hero, a headstrong heroine, and a mystery that keeps the pages turning.

From the Back Cover

She came to Redhaven for a fresh start and found a dead body instead. Now the only person standing between her and real danger is the one man she absolutely cannot stand. Lucas Graves is bossy, overprotective, and impossible to ignore. The line between enemy and everything is dangerously thin in this town.

About the Author

Nicole Snow is a Wall Street Journal and USA Today bestselling author who found her passion for writing during lunch breaks and plotting escapes from corporate boardrooms. With over a million books sold since her debut in 2014, she is known for crafting alpha heroes with hearts of gold, unbelievable suspense, and deeply emotional love stories. Amazon

Who Is This Book For?

This book is a perfect pick for adult romance readers who love a grumpy-sunshine dynamic, small-town settings with a dark twist, and stories where the tension crackles from page one. If you enjoy authors like Elsie Silver, Hannah Grace, or Devney Perry, this series will feel like home.

Frequently Asked Questions (FAQs)

Q1. Is The Broken Protector part of a series, or can it be read as a standalone? The Broken Protector is Book 1 in the Dark Hearts of Redhaven series by Nicole Snow. While it works as a standalone with a complete love story and satisfying ending, reading it first sets you up perfectly to enjoy the rest of the series. Each book follows a different couple in the same small town.

Q2. What genre is The Broken Protector? Is it just romance or does it have suspense too? It is a contemporary romance with a strong romantic suspense element. Readers get a grumpy-sunshine enemies-to-lovers love story alongside a genuine mystery and real danger. So if you love romance that also keeps you guessing, this book delivers both.

Q3. Is this book appropriate for all ages? The Broken Protector is written for adult readers aged 18 and above. It contains mature themes, steamy romantic content, and suspenseful situations. It is not suitable for children or younger teens.

Q4. How many pages is the paperback edition of The Broken Protector? The paperback edition runs 442 pages, published in 2023 by independently published under ISBN 9798399173085. AbeBooks It is a full-length novel that gives you plenty of story, drama, and romance to sink into.

Additional information
Weight450 g
Dimensions5 × 1 × 8 cm
Short Summary

Delilah moves to the quiet town of Redhaven looking for peace, but her new home holds a dark secret right from the start. Sheriff Lucas Graves steps in to protect her, even though the two of them clash at every turn. The story blends enemies-to-lovers fire, hilarious sweetness, heart-thumping suspense, and all the butterflies into one big feel-good small-town romance. Goodreads Beneath the banter and push-and-pull attraction, both characters carry real wounds, and watching them slowly open up to each other is what makes this story so worth reading.

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Research Methods in Psychology by Beth Morling
Research Methods in Psychology by Beth Morling Original price was: $149.99.Current price is: $39.99.
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Don't Make Me Think, Revisited by Steve Krug
Don't Make Me Think, Revisited by Steve Krug Original price was: $27.49.Current price is: $24.48.

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Machine Learning in Finance by Matthew F. Dixon

Original price was: $66.79.Current price is: $34.99.

Binding: Paperback
Language: English
ISBN-13: 9783030410704
Reader’s Age: Adults 18+ (Finance Professionals, Data Scientists, Students)
Ships Within: 5–10 Business Days
Author: Matthew F. Dixon, Igor Halperin, and Paul Bilokon
Release Date: Science & Technology
Genre: July 2, 2021

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Description

Transform Your Financial Career with Practical Machine Learning Techniques and Real-World Applications

About the Book

Machine Learning in Finance is your definitive roadmap to applying advanced computational techniques in real-world financial scenarios. Written by industry experts, this book demystifies complex algorithms and shows you exactly how to implement them for trading, risk assessment, portfolio management, and fraud detection. You’ll move beyond theoretical concepts to gain hands-on experience with Python-based implementations that mirror what’s used on Wall Street today. This isn’t just another textbook—it’s a practical toolkit designed to give you a competitive edge in quantitative finance.

From the Back Cover

“The intersection of finance and machine learning represents the future of the industry. This book provides the essential knowledge and practical skills to thrive in that future.”

About the Author

Matthew F. Dixon, Igor Halperin, and Paul Bilokon bring decades of combined experience from academia and the financial industry. Their expertise spans quantitative research, algorithmic trading, and financial engineering at leading institutions. As educators and practitioners, they understand both the theoretical foundations and real-world challenges that financial professionals face when implementing machine learning solutions. Their teaching approach makes complex concepts accessible without sacrificing depth or rigor.

Who Is This Book For?

This book is perfect for quantitative analysts, data scientists entering finance, portfolio managers, risk analysts, and graduate students in financial engineering or computational finance. If you’re looking to enhance your skill set with machine learning techniques that directly apply to financial markets, this is your go-to resource. Whether you’re working at a hedge fund, investment bank, fintech startup, or pursuing advanced research, you’ll find practical strategies you can implement immediately to improve trading performance, optimize portfolios, and manage risk more effectively.

What You’ll Learn and Gain

You’ll master supervised and unsupervised learning techniques tailored specifically for financial applications, understand how to build and backtest trading strategies using neural networks and reinforcement learning, and discover methods for risk prediction and portfolio optimization. The book covers everything from fundamental regression models to advanced deep learning architectures, complete with Python code examples and real market data. By the end, you’ll have the confidence to design, implement, and deploy machine learning systems that solve actual financial problems.

Frequently Asked Questions (FAQs)

Q: Do I need advanced programming skills to understand this book?
Basic Python knowledge is helpful, but the authors provide clear explanations and code examples that make concepts accessible. If you’re comfortable with fundamental programming and have a finance background, you’ll be able to follow along and implement the techniques.

Q: How is machine learning used in finance and trading?
Machine learning powers algorithmic trading systems, predicts market movements, optimizes portfolios, detects fraud, assesses credit risk, and automates financial decision-making. This book covers all these applications with practical implementation guidance.

Q: Is this book suitable for beginners in machine learning?
While some ML familiarity helps, the book starts with foundational concepts before advancing to complex techniques. It’s designed for finance professionals wanting to add ML skills and data scientists entering finance—making it accessible yet comprehensive.

Q: What makes this different from other machine learning finance books?
This book emphasizes practical implementation over pure theory, providing working code examples and real-world case studies. The authors focus on techniques actually used by quantitative hedge funds and investment firms, not just academic exercises.

Additional information
Weight110 g
Dimensions24.0 × 3.0 × 17.0 cm
Short Summary

Discover how to leverage machine learning algorithms to solve real financial challenges, from building profitable trading systems to managing risk effectively. This comprehensive guide takes you through supervised learning, deep neural networks, reinforcement learning, and natural language processing—all applied specifically to finance. You'll gain practical skills in algorithmic trading, portfolio optimization, derivative pricing, and market prediction using Python-based implementations that mirror industry standards used by leading financial institutions.

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