How the Resource System Works
Learn Resources
Five Core Modules keep terminology and references beside the chapter that gives them meaning. Module directories index all 60 chapter collections without replacing the chapter explanation or suggesting that a bibliography alone teaches the subject.
Explore Resources
Each Explore pathway keeps a dedicated Part 15 glossary, source directory and further-learning layer. Use current official documentation for product availability, versions, pricing and policy details that can change after publication.
Applied AI Resources
Practical resources stay attached to the build that uses them: setup guidance, APIs, validation, troubleshooting, project requirements and challenge criteria are indexed through Guided Labs, Portfolio Projects and Build Challenges.
Site-wide Transparency
Third-party attribution, disclaimer and navigation resources remain centrally available instead of being duplicated inside every learning page.
Learn Resources — Core Modules
All five Core Modules contain chapter-level Key Terms and References & Further Reading. A reference may be an official link, a named standard, a paper or a publication identifier. Where a stable authoritative destination materially helps verification, it should be linked; where a source is named without a link, use its title and issuing organisation to locate the current official version.
Module 1 — AI Foundations
AI vocabulary, agents, search, reasoning, applications, responsible-AI frameworks and foundational reading.
Open AI Foundations Resources →Module 2 — Technical Foundations
Python, mathematics, data work, development environments, reproducibility and official technical documentation.
Open Technical Foundations Resources →Module 3 — Machine Learning
Lifecycle, preprocessing, algorithm families, feature engineering, evaluation, deployment and scikit-learn references.
Open Machine Learning Resources →Module 4 — Neural Networks
Neural computation, backpropagation, optimisation, regularisation, debugging and PyTorch references.
Open Neural Networks Resources →Module 5 — Deep Learning
Deep architectures, Transformers, generative/foundation models, research papers, deployment and efficiency references.
Open Deep Learning Resources →Explore Resources — Pathways
Each Explore pathway retains its own Part 15 so terminology and changing ecosystem references stay close to the subject where they are taught. Product pages are useful for current facts; standards and research sources are stronger for durable definitions and evidence.
AI Models & Model Ecosystems
Model terminology, framework and infrastructure references, evaluation sources and further-learning routes.
Open AI Models Resources →Chatbots & AI Assistants
Assistant, RAG, memory, tools, agents, security, operations terminology and official references.
Open Chatbots & Assistants Resources →Other AI Tools & Resources
Tool-market terminology, verification workflow, official product sources and further-learning routes.
Open AI Tools Resources →Applied AI Resources
Applied AI resources are now tied directly to published builds. Use the Guided Labs for complete implementation references, Portfolio Projects for milestone-guided application work, and Build Challenges when you are ready to work from acceptance criteria alone.
Guided AI Labs — 8 Available
Python environment setup, complete reference implementations, validation, troubleshooting and extensions across assistants, RAG, tools, memory, agents, evaluation, multimodality and productionisation.
Open Guided AI Labs →Portfolio Projects — 6 Available
Project briefs, architecture questions, milestones and professional evidence standards without complete end-to-end solutions.
Open Portfolio Projects →AI Build Challenges — 6 Available
Independent briefs, constraints, limited hints, acceptance criteria and repository-ready evidence expectations.
Open AI Build Challenges →How to use implementation references: Read the linked Explore material for the durable pattern, then confirm current syntax, authentication, limits, pricing and deprecations in the relevant official documentation. A provider-specific example demonstrates one implementation; it does not define the concept or guarantee production suitability.
Site-wide Resources & Transparency
My Learning
For signed-in learners: resume recent work, review progress, bookmarks, skill coverage and completion readiness.
Open My Learning →Start Here
Choose your starting point and understand how Learn, Explore and Applied AI fit together.
Open Start Here →Third-Party Rights & Attribution
Review the central transparency register for third-party organisations, products, projects and protected names.
Open Rights & Attribution →Website Disclaimer
Review educational-use, accuracy, external-link and intellectual-property limitations for NIYAMA-VIDYĀ content.
Open Website Disclaimer →Terms of Use
Review learner-account responsibilities, acceptable use and platform rules.
Open Terms of Use →Accessibility
Review the accessibility approach, WCAG benchmark and inclusive-design practices.
Open Accessibility →Contact & Support
Find help with account, privacy, accessibility or learner-platform issues.
Open Contact & Support →A central reference directory
Use this page as the central directory for learning references across Learn, Explore and Applied AI. If a reference appears broken, outdated or unclear, report the specific issue so the relevant learning page can be reviewed.