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Mastering AI for Marketers
A practitioner-first curriculum for marketers — covering prompting, AI content creation, visual and multimedia tools, marketing agents, GEO/AEO search visibility, cookieless attribution, consumer trust, and AI-powered personalization and growth.
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About this program
AI has changed every layer of the marketing function — from how content is created and distributed, to how campaigns are measured, to how buyers discover and evaluate products. This cohort builds genuine AI fluency for marketers from the ground up. It starts with LLM fundamentals and the 2026 AI marketing stack, then covers the full practitioner skill set: prompting for brand voice, tone, and team workflows; AI-powered long-form content, social media, email, video scripts, and audio; image generation, AI video, voice cloning, and creative QA at scale; synthetic research, sentiment analysis, competitive intelligence, and trend prediction; building marketing agents for inbound, outbound, and campaign orchestration; GEO, AEO, and AI search visibility — making your brand quotable, building entity authority, and measuring AI-surface presence; data privacy, server-side tracking, consent mode, data clean rooms, and cookieless attribution; navigating consumer AI fatigue, anti-AI brand positioning, and advertising in AI interfaces; and the future of personalization, predictive marketing, AI-native growth loops, spatial computing, and org redesign. Ends with a capstone: a 12-month AI marketing transformation roadmap for your own organisation. 10 modules, 57 lessons, fully self-paced — no live sessions required.
Who is this for?
Marketers, content strategists, growth leads, brand managers, and marketing generalists who want to build practical AI skills across the full marketing function
What you'll actively build & learn
Understanding Fundamentals
Grasp the core mechanics of AI systems, from transformers to retrieval algorithms, moving beyond superficial APIs.
Production-Ready Architecture
Learn how to architect scalable, resilient generative AI applications that handle edge cases and high throughput.
Hands-on Engineering
Write custom PyTorch models, build multi-agent swarms using LangGraph, and deploy to Kubernetes.
Verifiable Execution
Complete rigorous capstone projects that serve as a proof-of-work portfolio for your next AI engineering role.
Time Commitment & Schedule
Self-Paced Modules
Flexible
No live sessions — work through all 10 modules whenever suits you, in any order you need.
Hands-On Labs
~20 hrs total
Capstone exercises in every module: prompt playbook, content ops system, campaign creative, agentic marketing system design, GEO audit, and a final 12-month transformation roadmap.
Module-Based Syllabus
Each module is structured around three things: what you'll cover, what capability you'll walk away with, and the concrete deliverable that moves you toward a working system of your own. Work through them in any order, at any pace.
10 self-paced modules, 57 lessons — work through them in order or jump to the skill you need most right now
A prompt playbook for your team, a working knowledge of the 2026 AI marketing tool landscape, and a 12-month AI transformation roadmap for your own marketing function
Practitioner-first lessons grounded in real tools, with capstone exercises that produce artefacts you can use immediately
AI Foundations for Marketers
- How LLMs work, the 2026 model ecosystem, the AI marketing stack, responsible AI risks, and mapping AI opportunities in your funnel.
A working mental model for AI capabilities and limitations — the foundation for every tool and workflow decision in the course.
An AI funnel audit mapping the highest-leverage AI opportunities in your own marketing function.
Prompting for Marketers
- Prompt anatomy, brand persona prompting, chain-of-thought and iterative prompting, system prompts for teams, and prompt anti-patterns.
The ability to write prompts that reliably produce on-brand outputs — and to build a reusable team prompt playbook.
A team prompt playbook with at least five reusable prompts for your most common marketing use cases.
AI-Powered Content Creation
- Long-form content, social media at scale, email sequences and personalization, video scripts and audio-first content, and quality gates to avoid AI slop.
The ability to run a content ops system that produces high-volume, on-brand content without losing editorial quality.
An end-to-end content ops system design for one content channel — brief to publish.
Visual & Multimedia AI
- Image generation tools (Midjourney, Firefly, DALL-E 4, Flux), AI video (Sora 2, Runway Gen-4, Kling 2, HeyGen), voice cloning and AI voiceover, and creative QA at scale.
The ability to produce multi-format campaign creative using AI tools without sacrificing brand consistency.
A multi-format creative brief executed with AI tools — including at least one image, one video concept, and one audio asset.
AI-Powered Research & Insights
- Synthetic research and AI focus groups, sentiment and voice-of-customer analysis, competitive intelligence, turning analytics into strategy with LLMs, and trend prediction.
The ability to run a continuous research and intelligence operation using AI — without waiting for quarterly research cycles.
A competitive intelligence brief built using AI monitoring tools, summarized and turned into a strategic recommendation.
AI Agents & Automation for Marketing
- What marketing agents are, no-code agent builders, AI for inbound, AI SDR agents for outbound at scale, campaign orchestration agents, machine customers, and a capstone design exercise.
The ability to design and deploy a marketing agent for at least one inbound or outbound workflow.
An agentic marketing system design — workflow diagram, tool selection, and a pitch-ready summary.
GEO, AEO & AI Search Visibility
- GEO vs.
- SEO vs.
- AEO, making your brand quotable for LLMs, entity authority and citation building, GEO across platforms, measuring AI-surface presence, and GEO content strategy.
The ability to diagnose and improve whether AI systems cite, recommend, and accurately characterise your brand and products.
A GEO audit across at least three AI surfaces (ChatGPT, Perplexity, Claude) with a citation improvement plan.
Data Privacy, Tracking & Attribution
- Zero-party and first-party data strategy, server-side tracking and Consent Mode v2, data clean rooms, AI-powered data activation, and cookieless measurement with MMM and incrementality.
A first-party data and measurement strategy that works in a cookieless world and satisfies privacy regulations.
A server-side tracking and consent implementation plan for one key conversion path.
Consumer Trust, AI Fatigue & Ethics
- What consumers actually think about AI in marketing in 2026, anti-AI brand positioning, using AI in decisions rather than content, IRL and experiential marketing, and advertising in AI interfaces.
The ability to make informed choices about where to show and where to hide AI in your brand and marketing — and why each decision matters.
A brand AI transparency policy: where you will use AI openly, where you will use it invisibly, and where you will not use it.
Personalization, Growth & the Future
- 1:1 personalization, predictive marketing (CLV, churn, next-best-action), AI-native growth loops, spatial computing and ambient AI, marketing org redesign, vendor evaluation, and the final capstone.
A 12-month AI marketing transformation roadmap that is grounded in your current stack and team — not a generic strategy deck.
A final capstone: a 12-month AI marketing transformation roadmap for your own organisation, submitted for the certificate.
The syllabus builds toward a final proof of work.
The weekly syllabus is designed to stack toward a capstone that demonstrates what you can actually build. By the end of the cohort, you are not just finishing modules. You are presenting a concrete output that ties the learning arc together.
View Alumni CapstonesIndustry-Grade Certification
Earn a credential that actually matters. Every certificate is tied to your Capstone Project repo, valid for life, and optimized for your professional technical profile.
View Certification TiersYour instructor
Anubhav Srivastava
Anubhav has spent the past two decades building machine learning and AI systems across startups, large enterprises, and high-scale consumer platforms. He has worked on patented AI technologies, authored books, and founded multiple ventures, and is currently building a deeptech startup focused on physical AI. Known for combining technical depth with practical thinking, he enjoys breaking down complex ideas into clear, accessible insights and is driven by a curiosity for how technology can solve real-world problems.
From our students
Engineers at different levels share what they built and what changed.
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Engineers trained
25+
Engineering leaders
40+
SaaS startups
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Alumni network
Alumni at
“The most technically rigorous program I've attended. No fluff — just pure deep-dives into transformer blocks and swarm logic. It's about understanding how LLMs actually work.”
Siddharth S.
Staff Engineer · Build Your Own LLM
“LangGraph and multi-agent orchestration was the missing link for our production pipeline. Essential for developers who need to move beyond single-prompt engineering.”
Elena R.
Senior AI Engineer · Agentic AI
“Direct access to instructors who are actually shipping AI products. The focus on evals-driven development is unique — we implemented their RAG evaluation approach across our entire startup.”
Arjun R.
Tech Lead · Claude Code
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