Introduction to Machine Learning

Description

Introduction to Machine Learning with Security: Theory and Practice Using Python in the Cloud (Second Edition, 2025) by Pramod Gupta is a groundbreaking guide to secure AI development. This book explores Introduction to Machine Learning, focusing on integrating security with intelligent systems. Readers will learn how to build robust, scalable, and attack-resistant machine learning models.

The book introduces fundamental concepts in supervised, unsupervised, and reinforcement learning, explaining their applications in real-world security challenges. It covers Python-based implementations of AI algorithms for fraud detection, anomaly detection, and cyber threat analysis. Introduction to Machine Learning provides step-by-step instructions for training secure models in cloud-based environments.

Designed for data scientists, security analysts, and AI engineers, this book bridges the gap between machine learning theory and cybersecurity applications. Readers will explore techniques for data encryption, model authentication, and adversarial defense mechanisms. The book discusses strategies for protecting AI systems from data poisoning, evasion attacks, and model theft.

Textbook offers insights into privacy-preserving AI, including federated learning, homomorphic encryption, and differential privacy. Readers will learn how to deploy secure AI models on AWS, Azure, and Google Cloud. The book explains compliance requirements for GDPR, HIPAA, and cybersecurity frameworks.

This book is a must-read for professionals interested in AI security, ethical hacking, and cloud-based ML deployment. It provides hands-on case studies and coding exercises for developing and securing AI applications. Introduction to Machine Learning prepares readers for the evolving landscape of AI-driven security solutions.