Machine Learning practice dataset
Explore 800 synthetic observations for repeat-purchase classification, with fixed splits and a Python baseline.
Explore 800 synthetic observations for repeat-purchase classification, with fixed splits and a Python baseline.
Document the problem, data, experiments, evaluation, and reproducible setup for an AI project.
Review the work from problem definition to evaluation and handover, recording evidence and follow-up actions.
Practice Python data types, functions, CSV loading, validation, and basic metrics with runnable examples.
Choose metrics, calculate a confusion matrix, and compare models using a repeatable evaluation protocol.
Plan a support-ticket classification project using a worked brief and an editable structure.
Start with 24 transactions, then analyze 1,200 synthetic orders linked to customer records.
Use 120 synthetic customer profiles to practice segmentation, joins, and customers with no transactions.
Analyze 180 campaign observations with funnel, cost, and attribution definitions.
Use an editable workbook with starter data, revenue formulas, and category summaries.
Build an evidence-based project narrative from a business question to recommendations.
Create a clear CV with projects, skills, and achievements that can be substantiated.
Practice formulas, conditional aggregation, lookups, and quality checks using sales examples.
Practice SELECT, aggregation, LEFT JOIN, common table expressions, and ranking in SQLite.
Write a concise report covering context, evidence, recommendations, limitations, and technical appendices.
Explore a campaign workbook with CTR, CPL, ROAS, overall summaries, and charts.
Track evidence of learning from data and statistics to reproducible projects.
Design instructions, context, examples, and evaluation for measurable generative AI tasks.
Use patterns for summarization, extraction, classification, and code review with example inputs and acceptance criteria.
Plan a generative assistant or application with task boundaries, sources, evaluation, and action controls.
Understand core LLM terms through examples and common misconceptions.
Connect text, images, audio, or video to a clearly defined user decision.
Review factual accuracy, sources, formatting, safety, and usefulness with a consistent rubric.
Compare prompt and model changes fairly using a log of results, cost, and decisions.
Summarize user goals, behavior, barriers, and context using evidence rather than stereotypes.
Map the experience from discovering a course to starting learning, including barriers and opportunities.
Describe registration decisions, payment paths, and error recovery in an editable diagram.
Plan desktop and mobile course-detail screens with information hierarchy, calls to action, and access states.
Prepare, moderate, document, and synthesize task-based usability sessions.
Align the problem, research scope, deliverables, constraints, and design success criteria.
Explain the design process from evidence of a problem to iteration and test results.
Review goals, content, navigation, accessibility, responsiveness, and interface states.
Navigate accounts, course access, learning, assignments, support, and privacy.
Follow the 20 Introduction to Artificial Intelligence units with practice and evaluation checkpoints.
Track topic status, practice time, evidence of learning, and next actions in a workbook.
Plan seven days of study with goals, time allocation, deliverables, and reflection.
Review concepts, practice, equipment, and rules before starting an assessment.
Connect your problem, code, results, tests, and reflection in a clear submission.
Understand eligibility, issuance, name checks, PDF files, and public verification choices.
Explore the learning areas, practical experience, and ways to start with Pyronyx Academy.
Browse a snapshot of 37 courses with levels, scope, prerequisites, projects, and availability.