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This comprehensive, practitioner-focused course progresses from core information security principles to advanced, AI-augmented red and blue team tradecraft. Learners explore ethical and legal foundations, reconnaissance, scanning, enumeration, and vulnerability analysis before moving into system hacking, covert operations, and modern web and API exploitation. The curriculum dives into malware analysis, encrypted traffic inspection, social engineering with deepfake risks, and DoS/DDoS with botnet orchestration. Cloud, containers, IoT/OT, and serverless security are addressed alongside risk management, threat modeling, and incident response. Throughout, the course examines how Generative AI accelerates both automated exploit generation and defensive detection, emphasizing responsible use, governance, and measurable outcomes. Hands-on content is framed for lawful, controlled environments and professional security practice.

CMPT 280: Intermediate. This is created by and for the community of students at University of Saskatchewan, Canada

This comprehensive course explores James Clear's bestselling book 'Atomic Habits', presenting scientifically-backed strategies for building good habits and breaking bad ones. Participants will learn the psychology of habit formation, practical techniques for cultivating positive change, and how tiny adjustments can lead to remarkable results. Through structured lessons and practical exercises, students will gain actionable insights to transform their personal and professional lives by mastering the power of atomic habits.

Ashtavakra Gita (or 'Ashtavakra Samhita') is a dialogue between Ashtavakra and Janaka (king of Mithila) on the nature of soul, reality and bondage. It offers an extremely radical version of non-dualistic philosophy. The Gita insists on complete unreality of external world and absolute oneness of existence. It does not mention any morality or duties and therefore, is seen by commentators as 'godless'. It also dismisses names and forms as unreal and a sign of ignorance.

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This comprehensive Grade 8 Science course covers essential concepts in life science, physical science, and earth and space science. The curriculum includes thorough explorations of cells, tissues, organs, organ systems, optics, forces, fluids, and water systems on Earth. Students will engage in hands-on experiments, comparisons, and analyses that not only deepen their scientific knowledge but also emphasize the interdependence of scientific principles in real-world applications. This course aims to foster critical thinking and understanding of how science relates to various careers and environmental issues.

This course provides a comprehensive understanding of Artificial Intelligence (AI), breaking down complex concepts into digestible lessons. It is designed for individuals who want to explore AI's potential, its applications across various industries, and its impact on society. Participants will learn about the fundamental principles of AI, machine learning, natural language processing, ethics, and the future of AI technologies. By the end of the course, learners will have a solid foundation in AI, empowering them to apply these insights in their personal and professional lives.

This comprehensive course on Managerial Accounting is designed to equip learners with the tools and knowledge necessary to make informed financial decisions within a business environment. Covering everything from basic cost concepts to advanced performance measurement techniques, this course will provide a detailed understanding of how managerial accounting aids in strategic decision-making. Suitable for aspiring managers, entrepreneurs, and accounting professionals, this course will enhance your ability to use accounting data for effective business planning and control.

This comprehensive course on Machine Learning is designed to provide learners with a strong foundation in the principles and applications of machine learning technologies. From understanding the basic concepts to implementing complex algorithms, this course will guide students through the entire machine learning lifecycle, including data preparation, model selection, evaluation, and deployment. Learners will engage with real-world examples and hands-on projects, equipping them with the skills necessary to tackle machine learning challenges in various domains.