Learn AI by doing

Video-first courses that turn AI tools into repeatable skills.

Learn AI by doing

Video-first courses that turn AI tools into repeatable skills.

• Practical Data Analysis with Python and Pandas - Build a practical analysis workflow in short steps: prepare a notebook, understand Python values, load common files, select and join data, summarize groups, and inspect a dataset. The selected lessons are demonstrations from Alex The Analyst’s YouTube channel; they are not a substitute for current library documentation. Learners finish with a reproducible exploratory-analysis notebook and a clear record of assumptions.
• Machine Learning Foundations: Algorithms to Pipelines - Connect the geometric intuition of vectors to nearest-neighbor reasoning, boosted trees, loss functions, preprocessing pipelines, and two compact neural-network examples. The sequence uses CampusX YouTube lessons as worked demonstrations. It emphasizes asking what data, objective, split, and evaluation measure a model uses, rather than treating a code example as evidence that a model will generalize.
• Deep Learning and Computer Vision with CNNs - Move from the role of deep learning and neural-network families to PyTorch fundamentals, convolutional structure, pretrained models, and transfer learning. The final lesson supplies a bounded image-classification project context. The material is presented through CampusX YouTube demonstrations, so learners should verify framework APIs and dataset terms against current documentation before implementing a project.
• NLP Foundations to Transformers and LLMs - Build an NLP mental model in sequence: define the task, place preprocessing and representation in a pipeline, try a supervised text-classification framing, then connect self-attention to the historical development of language models. The selected CampusX lessons are instructional source material rather than a promise of a particular model outcome. Exercises focus on making preprocessing choices and evaluation assumptions visible.
• Build GenAI Apps with LangChain and LangGraph - Start with the role of LangChain in generative-AI applications, then examine components, model interfaces, prompts, structured outputs, and parsers before moving to a LangGraph agentic workflow. CampusX provides the selected demonstrations and code context; package names and APIs can change, so treat the exercises as patterns to verify rather than immutable production instructions. The course rewards explicit schemas, test inputs, and observable state transitions.
• AI Automation & Agent Operations - Study practical automation patterns without treating a demo as a production blueprint. The course moves from an end-to-end content workflow through remote operation, scheduling, extensions, supervisor-style multi-agent designs, and an n8n workflow with human quality control. Learners are expected to test permissions, failure recovery, data handling, and change-sensitive tool steps.
• Claude Cowork & AI Knowledge Work - Use the selected demonstrations to study knowledge-work patterns: organizing files, adding durable context, researching a website, packaging reusable capabilities, drafting business artifacts, and comparing flows with agents. The emphasis is on inspectable inputs and reviewable outputs, because interfaces and permissions can change.
• Applied Prompting for Research & High-Stakes Work - Move from prompt structure and examples to legal-process context, conversation-gap analysis, retrieval grounding, visual research, and keyword discovery. The course treats model output as a draft for inspection—not as legal advice, a finding of fact, or a substitute for qualified review. Each assignment produces a prompt, evidence record, or evaluation artifact that can be audited.
• LLM Foundations: Deep Learning, Models & Retrieval - Connect neural-network training and transfer-learning practice with language-model internals, model evaluation, prompting, agent loops, MCP tool connections, and retrieval-oriented workflows. The material spans long-form technical lessons and shorter demonstrations; use the assignments to distinguish durable concepts from version-sensitive commands and to test every system boundary.
• AI & LLM Foundations to Research Agents (Urdu-English) - Build a conceptual path from machine learning and NLP to large language models, pretrained model tooling, prompting, and a Python research-agent capstone. The sequence is presented in Urdu-English instruction and should be used as a foundation for experiments, not as a promise that a model API, library, or hosted service will remain unchanged. Assignments emphasize source handling, structured outputs, and human review.
• AI Image Creation: Prompts, Consistency, and Local Tools - Work from a visual brief to a reviewable image workflow. You will compare generation and editing approaches, practice prompt structure, preserve character and scene continuity, and test hosted and local options without treating a particular interface as permanent. The course emphasizes version records, permissions, artifact review, and disclosure for synthetic media.
• AI Video Production: From Image-to-Video to Cinematic Ads - Plan short AI video work as shots, tests, and review checkpoints. The sequence moves from text-to-video prompting through cinematic planning, image animation, image-to-video experiments, local setup and troubleshooting, and an ad-oriented capstone. Durable production habits—briefs, continuity sheets, provenance records, defect logs, and human approval—remain useful as tools and models change.
• AI Content and Marketing Workflows - Build a small content system rather than a pile of tool tips. You will practice prompt and context design, multi-output briefs, bounded automation, UGC-style drafts, mobile shortcuts, marketing experiments, and controlled prompt labs. Each workflow keeps verification, privacy, claims review, and human approval visible so that a changing product UI does not become the course’s only source of truth.
• AI Research and Productivity Systems - Design research and productivity routines as auditable workflows. The lessons cover workspace permissions, meeting capture, scheduled research agents, product-signal collection, feature evaluation, agent security, and voice-assisted work. Rather than promising automatic accuracy, the course asks learners to define source boundaries, check outputs, protect sensitive information, and retain a manual fallback.
• Responsible AI: Fairness, Explainability, Privacy, and Trust - Learn to turn responsible-AI concerns into concrete review questions and decision records. The sequence moves from ethical scoping and competing fairness definitions to bias-test design, explanation limits, privacy threat modeling, copyright and provenance questions, and deepfake verification. It does not provide legal advice or a universal fairness score; it emphasizes evidence, affected people, uncertainty, human oversight, and escalation.

Frequently Asked Questions

Are there free courses available?

Yes, Starter courses and introductory lessons for all full courses are available as a free preview. No credit card is required to start learning.

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The Academy Pass grants you unlimited access to all full courses, premium resources, and exclusive assignments. It is available for $29/month or $249/year, and you can cancel anytime through our Stripe billing portal.

Is Academy included with Lumi Pro?

Yes. If you have an active Lumi Pro subscription, unlimited access to the One AI Guide Academy is automatically included at no extra cost.

Open-license course content · CC BY licensed · license verified

Only courses with complete, approved licensing evidence and at least one eligible published lesson are included in this catalogue.