Why just use AI when you can learn how to understand, develop, and deploy it? "Artificial Intelligence and Machine Learning" by Riggins and Osei helps you move from AI curiosity to real-world capability through hands-on activities, relatable examples, and a semester-long “AI Agent” project. Offering flexible pathways for both beginners with no-code tools and advanced learners working in Python, this comprehensive resource teaches you how to progressively design, build, evaluate, and deploy an AI solution while learning the complete AI lifecycle. With a focus on Explainable AI, ethics, and practical problem-solving, you'll gain the knowledge, skills, and confidence to understand, create, and responsibly apply AI across any field.
Introduction to Artificial Intelligence
Ethical Impact of AI
Introduction to Machine Learning
Programming for Machine Learning Applications
Data for Machine Learning
Understanding Math Fundamentals for Machine Learning Applications
Computer Vision
Natural Language Processing
Reinforcement Learning
Deep Learning: A Subset of AI and a Key Technology Driving Modern AI Systems
Neural Networks: A Foundational Concept within AI and Central to Machine Learning
Future of AI & Machine Learning
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Shakia Riggins
Sha’Kia M. Riggins, MSIT is a nationally recognized leader in artificial intelligence education, applied machine learning, and ethical AI implementation. She currently serves as the Dean of Information Technology at Florida State College at Jacksonville (FSCJ), where she leads strategic initiatives that position the institution at the forefront of workforce-aligned AI education. A U.S. Navy veteran and seasoned technologist, Riggins brings more than three decades of experience spanning industry, entrepreneurship, and academia. She is the CEO of Data B.I.T.S., LLC and has been instrumental in establishing FSCJ as an East Coast AI Center of Excellence, developing innovative degree programs, technical certificates, and industry partnerships that expand access to AI education. Riggins is the author of Artificial Intelligence and Machine Learning: Concepts, Applications, and Ethical Considerations (1st Edition), a foundational text that bridges theory, real-world applications, and responsible AI practices. Her work emphasizes ethical frameworks, workforce readiness, and inclusive innovation, and she is frequently sought after by publishers and industry to shape next-generation AI curriculum and learning tools. She serves as a Lead Facilitator for Intel’s Digital Readiness and AI Workforce Programs, helping train educators nationwide, and is an active member of the National Applied AI Consortium for Educators, IEEE, and multiple AI advisory boards. Her collaborations with global industry partners have influenced applied AI education both nationally and internationally. A passionate advocate for mentorship and equity in technology, Riggins is committed to preparing the next generation of AI leaders while ensuring that innovation is guided by ethics, accountability, and social impact.
- Baffour Osei
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Connect every chapter to a meaningful, real-world outcome through a semester-long AI agent project.
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Help students identify bias, hallucinations, and flawed outputs with built-in XAI tools and critical auditing exercises.
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Support business-focused learners with no-code tools, as well as technical students with Python and machine learning.
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Move beyond the algorithm by teaching students to build, deploy, monitor, and optimize real AI systems from concept to production.
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Transform AI math from a barrier into a bridge with concept-first explanations that make complex ideas accessible and memorable.
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Harness AutoML and AI-assisted coding to boost productivity while building programming, debugging, and critical thinking skills.
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Make computer science concepts stick with interactive videos, visualizations, and practice tools in MindTap that drive greater understanding.
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Help students explain, analyze, and apply AI responsibly with Applied AI Skill Video Recording Assessments that build AI literacy and career-ready skills.
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