Artificial Intelligence

A. Product Name & One-Line Positioning

Product name: Advanced Professional Certificate in Applied Artificial Intelligence (APC-AI)
Positioning (one line): A career-focused AI training program that teaches applied machine learning, generative AI, and production deployment skills through hands-on labs, real projects, and mentorship — for professionals who want to design, build and lead AI initiatives.

B. Target Audience & Market Fit

• Primary: Mid-career professionals (data analysts, software engineers, product managers, marketers) aiming to transition into AI roles or lead AI projects.

• Secondary: Early-stage founders, consultants, and team leads who must implement AI in products or workflows.

• Prerequisites: Basic programming (Python), high-school math (algebra), familiarity with data concepts. Offer a prep module for beginners.

• Pain points solved: Lack of applied skills, difficulty deploying models to production, inability to craft business cases for AI, and lack of mentorship.

C. Transformational Learning Outcomes (What graduates will be able to do)

By program end students will be able to:

1. Design AI solutions aligned with business objectives and ethics.

2. Build, train and evaluate machine learning models (supervised, unsupervised, basic deep learning).

3. Build production-ready ML pipelines and deploy models (APIs, containers).

4. Apply generative AI (LLMs) safely for automation, content & product augmentation.

5. Measure model performance, monitor drift, and set up observability.

6. Communicate AI outcomes to stakeholders and lead AI project roadmaps.

7. Deliver a capstone project that demonstrates business impact.

D. Curriculum Structure — Modules & Lesson Breakdown (Recommended 12-module flagship cohort)

Module 0 — Prep & Foundation (self-paced, 1 week)

• Python refresh (functions, pandas, numpy)

• Git basics & environment setup

• Math essentials (probability, linear algebra basics)

Module 1 — AI Fundamentals & Strategy (1 week)

• AI vs ML vs DL; AI lifecycle

• Business problem framing & metrics

• Ethics, bias, and governance overview

Module 2 — Data Engineering for AI (2 weeks)

• Data collection, cleaning, feature engineering

• Databases, data lakes, ETL basics

• Hands-on: building a data pipeline (CSV → cleaned dataset)

Module 3 — Supervised Learning (2 weeks)

• Regression and classification fundamentals

• Feature selection, cross-validation, hyperparameter tuning

• Hands-on: build & evaluate a classification model

Module 4 — Unsupervised Learning & Dimensionality (1 week)

• Clustering, PCA, anomaly detection

• Use cases & interpretation

Module 5 — Deep Learning Essentials (2 weeks)

• Neural network basics, CNNs for imagery, RNNs/transformers intro

• Hands-on: simple image/text model with transfer learning

Module 6 — Model Evaluation & Interpretability (1 week)

• Metrics, confusion matrix, ROC, precision/recall, calibration

• SHAP, LIME, explainable AI practices

Module 7 — Generative AI & LLMs (2 weeks)

• LLM fundamentals; prompt engineering best practices

• Fine-tuning vs. retrieval-augmented generation (RAG)

• Hands-on: build a retrieval-augmented Q&A agent

Module 8 — ML in Production (2 weeks)

• APIs, Docker, model serving (FastAPI, TorchServe), CI/CD for ML

• Feature stores, model registries, blue-green deploys

Module 9 — Monitoring, Ops & MLOps (1 week)

• Model drift, observability, logging, data validation

• SLOs, alerting, rollback strategies

Module 10 — Responsible AI & Security (1 week)

• Data privacy, GDPR basics, adversarial concerns, secure ML pipelines

Module 11 — Productization & Business Cases (1 week)

• Building an AI product roadmap, cost estimates, ROI, KPI definition

• Go-to-market & stakeholder comms

Module 12 — Capstone Project (3–4 weeks)

• Real dataset & business problem (partner with industry or internal brief)

• Deliverables: problem statement, model, production demo, metrics, slide deck & 10-minute demo video

E. Course Format, Delivery & Learning Experience

• Format: Blended cohort (weekly live workshops + self-paced labs + office hours + mentorship).

• Weekly cadence: Pre-work (2–3 hrs), live workshop (90–120 min), practical lab (3–5 hrs), optional office hours.

• Mentorship: 1:10 ratio mentor groups; monthly 1:1 mentorship for capstone.

• Learning Tech: LMS for content (SCORM/video), GitHub for code, cloud environment for labs (Google Colab / AWS Educate), Slack/Discord community.

• Assessments: Weekly labs, quizzes, peer reviews, mentor feedback. Pass = 70% aggregate + approved capstone.

F. Duration, Cohorts & Delivery Options

• Flagship cohort: 16 weeks (recommended)

• Intensive bootcamp: 8 weeks (full-time)

• Self-paced: 6 months access (no live mentorship)

• Micro-credentials: 4-week focused short courses (e.g., “LLM Prompting & RAG”)

G. Assessment, Badging & Certification

• Assessments: Auto-graded quizzes + practical labs + capstone evaluation rubric.

• Certification: “Professional Certificate in Applied AI — Legacy Institute” (include unique cert ID, QR to verify).

• Badges: Module completion badges (OpenBadges format) for LinkedIn.

• Criteria: Pass quizzes, complete 80% labs, approved capstone.

H. Capstone & Project Requirements

• Problem selection: Real business problem with measurable KPI.

• Deliverables: Problem brief, dataset prep, model notebook, deployment demo (API/streamlit), monitoring plan, slide deck, 10-minute recorded pitch.

• Evaluation rubric: Business impact (30%), technical soundness (30%), reproducibility/code quality (20%), presentation & documentation (20%).

I. Instructor & Mentor Profiles (hiring & onboarding)

• Lead instructor: Senior ML engineer/PhD or equivalent with production ML experience.

• Mentors: Practitioners (2–5 years ML/AI work), TAs for labs.

• Guest lecturers: Product managers, legal experts, industry partners.

• Onboarding brief: Teaching guide, grading rubric, session plans, recordings policy.

J. Technology Stack & Tools

• Code/Notebooks: Python, Jupyter, Colab, PyTorch/TensorFlow, scikit-learn.

• Deployment: FastAPI, Docker, Kubernetes (optional), AWS/GCP/Azure examples.

• Data & storage: PostgreSQL/s3, feature store (Feast example), MLflow or DVC.

• LMS & community: Teachable/LearnDash/Coursera LMS + Slack/Discord + Zoom.

• CI/CD: GitHub Actions for tests, model registry integration.

• Monitoring: Prometheus + Grafana examples; error/metric tracking (Sentry/Weights & Biases).

K. Content Development Process (how to build course materials)

1. Define outcomes per module.

2. Create lesson outlines & slide decks.

3. Produce short video lessons (6–12 mins) + demo notebooks.

4. Author detailed lab assignments with starter code.

5. Build quizzes & answer keys.

6. Record live workshop scripts & dry runs.

7. Peer review internal QA (technical & pedagogic).

8. Pilot with beta cohort, iterate.

L. Pricing Strategy & Packages

• Self-paced: $299–$599 (access + certificate)

• Cohort (standard): $1,499–$2,999 (live sessions + mentorship + capstone)

• Cohort (premium): $3,999–$7,499 (1:1 mentorship, guaranteed interview prep, employer introductions)

• Installments & scholarships: 3–6 month payment plans + partial scholarships for diversity initiatives.

• Corporate & team licensing: Custom pricing per seat + private cohorts.

(Adjust pricing to local market and brand positioning.)

M. Sales Funnel & Marketing Plan (step-by-step)

Top of funnel (awareness):

• Thought leadership articles (AI strategy, case studies).

• Short explainer videos & demo clips (YouTube, LinkedIn).

• Free webinar: “How to Deliver an AI Project in 30 Days” (lead magnet).

Mid funnel (engagement):

• Free mini-course (email capture) + workbook.

• Case studies & student success videos.

• Live Q&A sessions & office hours.

Bottom funnel (conversion):

• Cohort calendar + limited seats urgency.

• Demo capstone day invites (attend past student project demos).

• 1:1 consultation calls for enterprise leads.

Channels: LinkedIn Ads, Google Search Ads (targeted keywords), partner newsletters, podcasts.
Metrics: CPL, conversion rate, CAC, LTV, cohort fill rate.

N. Website & Sales Page Content Structure (high-impact)

1. Hero: Program name + one-line value + CTA (Apply/Enroll).

2. Outcomes: Bullet list of learning outcomes & career outcomes.

3. Curriculum: Module list + sample lesson topics.

4. Format & schedule: cohort dates & time commitment.

5. Capstone & credentials: sample projects & certificates.

6. Instructors & mentors: bios with social proof.

7. Testimonials & case studies (video + text).

8. Pricing & payment options + FAQs.

9. Apply/Enroll form + scholarship CTA.

High-impact CTA copy: “Apply for the next cohort — limited seats. Get a 15-minute advisory call.”

O. Sample High-Conversion Email Sequence (5 emails)

1. Welcome & value — deliver lead magnet, mini-course link.

2. Problem framing & outcomes — case study + invite to webinar.

3. Proof & urgency — testimonial + upcoming cohort dates.

4. Offer & FAQ — pricing, scholarships, payment plan.

5. Last chance — reminder, scarcity, instructor invite.

(Each email 150–250 words with prominent CTA.)

P. Community, Support & Alumni Strategy

• Community: Slack/Discord channels for modules, jobs, peer review.

• Office hours: Twice weekly mentor office hours.

• Career services: Resume workshop, mock interviews, job board.

• Alumni: Private group, ongoing masterclasses, alumni only discounts.

Q. Operations & Admin (day-to-day)

• Enrollment ops: Admissions team, automated confirmation flows, welcome kit.

• Student success: Dedicated student success manager per 50 students.

• Payments: Stripe/PayPal, invoicing for corporates.

• Scheduling: Calendar integration, timezone support recorded sessions.

R. Legal, Privacy & Compliance

• T&Cs: Refund policy, code of conduct, intellectual property (who owns capstone code).

• Privacy: Data processing agreement, cookie policy, GDPR compliance if EU students.

• Accessibility & copyright: Alt text for videos, captioning, license for third-party datasets.

S. KPIs & Success Metrics (what to measure)

• Academic: Completion rate, pass rate, average capstone score.

• Commercial: Conversion rate (lead→enroll), CAC, cohort fill rate.

• Impact: Graduate job placement rate, salary uplift, employer satisfaction.

• Engagement: Active daily users, forum posts, office hours attendance.

T. FAQ (samples for site & sales)

• Q: Who this course is for?
  A: Mid-career professionals, engineers, managers; beginners can join after prep module.

• Q: Do I need a math background?
  A: Basic algebra and understanding of statistics recommended; we provide refreshers.

• Q: Will I get a job?
  A: We provide career support but cannot guarantee employment; graduates often secure roles with projects shown in capstone.

U. Upsell, Stack & Career Pathways

• Upsells: 1:1 mentorship, enterprise consulting, advanced specialization tracks (Computer Vision, MLOps, LLM engineering).

• Stackable certificates: Offer modular micro-credentials that stack to the full certificate.

V. Industry Partnerships & Hiring Pipelines

• Partner with local tech firms for capstone briefs, guest lectures and interview pipelines.

• Offer corporate cohorts and white-label training for teams.

W. Student Onboarding Flow (detailed)

1. Enrollment confirmation & welcome email.

2. Pre-course survey (goals, background).

3. Access to LMS + Slack + GitHub repo.

4. Orientation live session + technical check.

5. Mentor assignment & cohort calendar.

X. Feedback Loop & Continuous Improvement

• Post-module surveys, NPS after cohort, monthly mentor reviews.

• Beta test new modules with a pilot group and iterate every 6 months.

Y. Resource Pack & Templates (what to include)

• Lesson slide decks, instructor notes.

• Starter notebooks, datasets, lab rubrics.

• Project templates (README, code of conduct, submission checklist).

• Marketing assets: banners, social posts, video trailers, email templates.

Z. Implementation Timeline & Checklist (12–16 week plan)

Weeks 1–2: Define outcomes, outline modules, hire lead instructor.
Weeks 3–6: Produce core curriculum (videos, labs), set up LMS.
Weeks 7–8: Build sales page, marketing assets, lead magnet.
Weeks 9–10: Pilot beta cohort (small group) + gather feedback.
Weeks 11–12: Finalize curriculum, certification, pricing.
Weeks 13–16: Launch marketing, open enrollment, first cohort start.