Fred Nwanganga – författare

Master every exam objective and AI cybersecurity concept for the CompTIA SecAI+ CY0-001 exam, complete with an online test bank, hundreds of practice questions, and digital flashcardsIn CompTIA SecAI+ Study Guide: Exam CY0-001, veteran cybersecurity and AI professionals Mike Chapple and Fred Nwanganga deliver easy-to-follow coverage of the security concepts critical to AI use and development. You’ll examine basic AI concepts as they relate to cybersecurity, how to secure AI systems, AI-assisted cybersecurity techniques, and AI governance, risk, and compliance issues necessary for working professionals in a variety of technical roles.This book provides authoritative discussions of the relevant issues you need to understand before you start a new career – or advance in your current one – in cybersecurity, with efficient and accurate content. You’ll also find: Three custom practice exams that get you ready to succeed on your first try at the CY0-001 and help you overcome test anxietyHundreds of review questions that measure your readiness for the certification exam, help you retain and remember key concepts, and identify knowledge gaps you need to address before you take the SecAI+ examComplimentary access to the online Sybex learning environment, complete with hundreds of additional practice questions (including two full-length practice exams), flashcards, and a glossary of key terms, all supported by Wiley's support agents who are available 24x7 via email or live chat to assist with access and login questionsPerfect for everyone planning to take the CompTIA CY0-001 exam or interested in pursuing a higher-level certification like the SecurityX, CISSP, or CISA, the CompTIA SecAI+ Study Guide is also a must-read for working cybersecurity professionals who want to brush up on AI-specific concepts and for everyone who’s ever wondered if IT security is right for them.

Guides professionals and students through the rapidly growing field of machine learning with hands-on examples in the popular R programming languageMachine learning—a branch of Artificial Intelligence (AI) which enables computers to improve their results and learn new approaches without explicit instructions—allows organizations to reveal patterns in their data and incorporate predictive analytics into their decision-making process. Practical Machine Learning in R provides a hands-on approach to solving business problems with intelligent, self-learning computer algorithms. Bestselling author and data analytics experts Fred Nwanganga and Mike Chapple explain what machine learning is, demonstrate its organizational benefits, and provide hands-on examples created in the R programming language. A perfect guide for professional self-taught learners or students in an introductory machine learning course, this reader-friendly book illustrates the numerous real-world business uses of machine learning approaches. Clear and detailed chapters cover data wrangling, R programming with the popular RStudio tool, classification and regression techniques, performance evaluation, and more.  Explores data management techniques, including data collection, exploration and dimensionality reductionCovers unsupervised learning, where readers identify and summarize patterns using approaches such as apriori, eclat and clusteringDescribes the principles behind the Nearest Neighbor, Decision Tree and Naive Bayes classification techniquesExplains how to evaluate and choose the right model, as well as how to improve model performance using ensemble methods such as Random Forest and XGBoostPractical Machine Learning in R is a must-have guide for business analysts, data scientists, and other professionals interested in leveraging the power of AI to solve business problems, as well as students and independent learners seeking to enter the field.