🎓 Scholarship — up to 50% off · Code SCHOLARSHIP 60:00 Claim Seat →

Cohort 11 Starts 11 July 2026 20 Masterclasses · 20 Weeks Weekend-Friendly · Live + Self-Paced 1-on-1 Mentorship CPD Member Org · 135+ Hours

Go from zero to deployed GenAI builder in 20 weeks.

The AI Residency is Decoding Data Science’s flagship mentor-led cohort — 20 masterclasses through LLMs, RAG, fine-tuning, agents, security and evaluation. Real projects. Real portfolio. Residents from 50+ countries.

Enroll in Cohort 11 → Show My Interest — 4 Details

Monthly payment plans available · 30-day satisfaction guarantee · Student & company scholarships*

Cohort 11 kicks off in — 11 July 2026 · 11:00 AM GST

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▶ Watch: Inside the AI Residency Program

150+

AI & Data events

180K+

Community ecosystem

50+

Countries of residents

3,000+

Careers supported

110+

5★ Trustpilot reviews

CPD Member Org

Tools & frameworks you’ll build with

🐍 Python🦜 LangChain🕸️ LangGraph🤗 Hugging Face⚡ GROQ🦙 LlamaIndex🧠 CrewAI🔁 Langflow🤖 AutogenAI🎯 Agno🎨 Gradio📊 Streamlit🚀 FastAPI🐳 Docker☁️ AWS EC2🪣 AWS S3🏗️ BentoML🔍 Opik🔗 n8n♊ Gemini🅰️ Anthropic⚡ Grok💬 OpenAI📌 MCP🗂️ Vector DBs🔬 RAG🎛️ Fine-Tuning🛡️ AI Security📐 GenAI Evals🐍 Python🦜 LangChain🕸️ LangGraph🤗 Hugging Face⚡ GROQ🦙 LlamaIndex🧠 CrewAI🔁 Langflow🤖 AutogenAI🎯 Agno🎨 Gradio📊 Streamlit🚀 FastAPI🐳 Docker☁️ AWS EC2🪣 AWS S3🏗️ BentoML🔍 Opik🔗 n8n♊ Gemini🅰️ Anthropic⚡ Grok💬 OpenAI📌 MCP🗂️ Vector DBs🔬 RAG🎛️ Fine-Tuning🛡️ AI Security📐 GenAI Evals

Free Live Webinar · Every Sat · 11:00 AM GST

Not sure if the Residency is for you? Start here — free.

Join AI Career Mastery: Learn & Build — a free session (worth $20) with a clear roadmap and practical steps to apply AI at work. Limited seats every Saturday.

Reserve Free Spot →

Tailored Tracks

Which outcome are you building toward?

Three pathways — same curriculum, different endgame. Your mentorship and project focus adapts to your track.

Track 01

💼

<< Get a job in Gen-AI >>

For Job Seekers

Build a killer portfolio of Gen-AI products. Get personalized mentoring on interview prep and resume building.

$15,000+ value in portfolio & career coaching

Track 02

🚀

<< Build a Gen-AI product >>

For Founders

Create or integrate a Gen-AI product with access to 100+ GenAI startup founders and industry experts.

$35,000+ value in product strategy & expert network

Track 03

🧰

<< Service businesses with Gen-AI >>

For Agency Owners

Deliver full-stack Gen-AI solutions to clients. Get guidance on pricing and lead generation for your agency.

$20,000+ value in operational training & consulting

For companies & startups: enroll your team and build real in-house AI capability. Program runs on weekends. Special company scholarships available.

💬 WhatsApp Us ✉️ Send Email

Curriculum

20 Masterclasses · 20 Weeks

The most beginner-friendly Generative AI curriculum — zero to pro. Click any card to expand the full syllabus for that masterclass.

Phase 1 — Foundations & First Builds

MC 00

Essential Prerequisite: Basic Python & What is Gen AI?

Get build-ready before Cohort 11 starts — understand AI and set up your Python environment.

● Pre-Work Open

Understanding Generative AI — what it is, GenAI in action (1.1–1.3)

Python Basics — essentials for beginners, writing your first program, control flow & functions (2.1–2.3)

Setting Up Your Environment — installing Python & IDEs, virtual environments, command line intro (3.1–3.3)

Pre-work: Generative AI Toolkit submission, GitHub profile assignment, Gradio UI exploration

MC 02

Application Architecture: UI Building, Git & GitHub

Ship like an engineer from week one — design UIs and manage code properly.

UI Design Principles — design essentials, tools for interfaces, prototyping your application (1.1–1.3)

Version Control with Git — intro to Git, common commands, resolving conflicts (2.1–2.3)

GitHub — setup & usage, collaborating with teams, managing repositories effectively (3.1–3.3)

MC 03

GROQ & Building Apps with APIs in Python

Build your first working AI app on real APIs.

1st Project

Understanding GROQ — intro, why it matters for AI apps, syntax & functions (1.1–1.3)

Working with APIs in Python — REST principles, making HTTP requests, parsing API responses (2.1–2.3)

Build Your First Python App — structuring the app, integrating APIs, error handling & debugging (3.1–3.3)

MC 04

Intro to LLMs: How They Work (Playground)

Build intuition for how large language models actually behave.

Understanding LLMs — how they generate text, open vs closed source, popular industry models (1.1–1.4)

Exploring the LLM Playground — setup & access, experimenting with prompts, evaluating outputs (2.1–2.3)

Practical Understanding — customizing prompts for tasks, limitations of LLMs, industry applications (3.1–3.3)

MC 05

Generative AI Configuration & Different LLMs

Go from playground to code — configure models and choose the right one.

2nd Project

GenAI Configuration Basics — key parameters (temperature, max tokens, top-p), customizing for tasks (1.1–1.3)

Comparing & Choosing LLMs — GPT, Claude, Llama; strengths, limitations & use cases; accessing APIs (2.1–2.3)

Build & Present Your 2nd Project — implement with configured LLMs, document choices, showcase to stakeholders (3.1–3.3)

MC 06

LLM Wrappers, Function Calls & Data Integration

Connect models to your data and external functions — mid-capstone project.

LLM Wrappers — purpose, designing for efficiency & scalability, tools & libraries (1.1–1.3)

Function Calls with LLMs — intro, implementing API calls for AI-powered apps, error handling (2.1–2.3)

Data Integration Techniques — connecting to external sources, preprocessing & cleaning, real-time pipelines (3.1–3.3)

Mid-Capstone Project — Weather or Stock Bot: define goals, build & test, document & present (4.1–4.3)

Phase 2 — RAG & Knowledge Systems

MC 07

Hugging Face: Building an Open-Source Project

Build and publish with the open-source AI ecosystem.

Hugging Face Ecosystem — overview & role in AI, key tools & libraries, pre-trained models & datasets (1.1–1.3)

Build Your Open-Source Project — repo setup on GitHub, selecting & fine-tuning pre-trained models, reusable docs (2.1–2.3)

Contributing to Open Source — contribution standards, code quality & docs, pull requests & community engagement (3.1–3.3)

Finalize & Showcase — testing & deploying, writing a clear README, presenting to the AI community (4.1–4.3)

MC 08

LlamaIndex: Retrieval-Augmented Generation (RAG)

Ground your AI in real documents and knowledge.

Understanding RAG — what it is & why it matters, benefits & use cases (1.1–1.3)

Intro to LlamaIndex — overview, setup & exploration, integrating with LLMs (2.1–2.3)

Build a RAG System — designing the pipeline, implementing the workflow, testing & optimizing (3.1–3.3)

Finalize & Present — documenting, deploying the RAG system, showcasing on LinkedIn & GitHub (4.1–4.3)

MC 09

Vector Embeddings & Vector Databases

The infrastructure behind every serious RAG system.

Understanding Vector Embeddings — what a vector DB is & why use it, building search-enhanced GenAI apps (1.1–1.3)

Intro to Vector Databases — designing your project, implementing & testing, setting up your first vector DB (2.1–2.3)

Integrating Vector DBs with GenAI — combining embeddings with models, search-enhanced apps, optimizing query performance (3.1–3.3)

Build & Deploy a Vector-Powered AI Solution — design, implement & test, deploy & demonstrate (4.1–4.3)

MC 10

Advanced RAG

Re-ranking, hybrid search, and production-grade retrieval patterns.

Deep Dive into RAG — revisiting fundamentals, advanced retrieval strategies, RAG in complex use cases (1.1–1.3)

Advanced Tools & Techniques — RAG with vector DBs, optimizing performance, handling large-scale datasets (2.1–2.3)

Integrating Advanced RAG with GenAI — combining with LLM APIs, context-aware systems, error handling & troubleshooting (3.1–3.3)

Design & Deploy an Advanced RAG Project — plan for real-world use, implement pipelines, showcase & deploy (4.1–4.3)

Phase 3 — Fine-Tuning & Reasoning

MC 11

Tokenization & Preprocessing Data for Fine-Tuning

Prepare data the way model trainers do.

Understanding Tokenization — what it is, types (word/subword/character), challenges & solutions (1.1–1.3)

Preprocessing Data for Fine-Tuning — cleaning & normalization, handling large & imbalanced datasets, formatting for LLMs (2.1–2.3)

Tools & Libraries — Hugging Face tokenizers, automating preprocessing with Python, multilingual & specialized data (3.1–3.3)

Fine-Tuning Ready — combining tokenization & preprocessed data, validating & debugging, finalizing the dataset (4.1–4.3)

MC 12

Data Preparation & Fine-Tuning (LoRA & QLoRA)

Customize models on your own datasets with parameter-efficient methods.

Intro to Fine-Tuning LLMs — why fine-tune, overview of approaches, challenges in fine-tuning (1.1–1.3)

PEFT (Parameter-Efficient Fine-Tuning) — what PEFT is, LoRA basics, advanced LoRA concepts (2.1–2.3)

QLoRA (Quantized LoRA) — intro, key optimizations, comparing LoRA & QLoRA (3.1–3.3)

Hands-On Fine-Tuning Project — dataset preparation, model training, deployment & monitoring (4.1–4.3)

MC 13

Chain of Thought & ReACT Prompting

Make models reason step-by-step and act on tools.

Chain of Thought (CoT) Prompting — what it is, applications in AI tasks, zero-shot vs few-shot CoT (1.1–1.3)

CoT in Math, Reasoning & Multi-Step QA — types of CoT, practical applications

ReACT Prompting — what ReACT is (Reason + Act framework), how it differs from CoT, building reasoning agents (2.1–2.2)

MC 14

LangChain

The framework powering modern LLM applications.

Intro to LangChain — what it is, key concepts & architecture, LangChain vs traditional LLM workflows, core components (1.1–1.5)

LangChain Basics — connecting to LLM providers (OpenAI, Hugging Face), prompt templates (static & dynamic), chains (LLMChain, SequentialChain), memory (2.1–2.4)

Agents & Tools — what LangChain agents are, ReAct agent & tool integration, ZeroShotAgent vs tool-using agent (3.1–3.3)

MC 15

LangChain Advanced + Deployment

Take your apps from notebook to live, production-ready deployment.

RAG in LangChain — chunking & embeddings (OpenAI & Sentence Transformers), vector stores (FAISS, Chroma, Pinecone), document loaders & text splitters (1.1–1.4)

APIs & Advanced Agents — calling & wrapping APIs, API-based agents (Weather/CryptoBot), modular apps with LCEL, custom agents & toolkits (2.1–2.4)

Deployment, Testing & Monitoring — frontend (Gradio/Streamlit), backend (FastAPI + LangChain), tracing & debugging with LangSmith (3.1–3.3)

Phase 4 — Agents, Security & Evaluation

MC 16

Multi-AI Agent Systems with CrewAI

Orchestrate teams of AI agents that collaborate on real tasks.

Intro to Multi-Agent Systems — what AI agents & multi-agent systems are, use cases (1.1)

Getting Started with CrewAI — framework overview, installation & setup, agents.yaml & crew.yaml files (2.1–2.3)

Designing Agents & Roles — defining roles/tasks/goals, creating specialized agents, writing effective collaboration prompts (3.1–3.3)

Orchestrating the Crew — running pipelines, sequential vs parallel execution, monitoring, logging & debugging agents (4.1–4.3)

Tool & Memory Integration + Real-World Project — external tools (APIs, DBs, browsers), short-term memory, AI Research Crew project (5.1–6)

MC 17

LangGraph

Build stateful, controllable agent workflows with graph-based logic.

Intro to LangGraph — what it is & why use it, core concepts (nodes, edges, states, agents), vs LangChain & CrewAI (1.1–1.3)

Setting Up LangGraph — installation, project structure, key components (State, Graph, ToolNode, LLMNode) (2.1–2.3)

Designing Workflows — creating & registering nodes, state objects & transitions, graph topology (sequential, branching, cyclic) (3.1–3.3)

Hands-On Mini Project — build a task-routing agent (LLM + tool + if-else branching), add external tool/API, run, test & optimize (4.1–4.3)

MC 18

Model Context Protocol & Agent-to-Agent

The newest standards connecting agents, tools, and data across systems.

Intro to MCP & Agent Collaboration — what MCP is, core concepts (clients, servers, tools, resources, prompts), MCP vs function calling/plugins/webhooks (1.1–1.4)

Build & Test an MCP Server — scaffold a minimal Python server, add one tool + one resource (schemas, idempotency, guardrails), wire a client (2.1–2.3)

Agent-to-Agent Mini-Workflow — Researcher↔Writer roles & shared context (Blackboard), flow logic & routing, safety & budgets, observability (3.1–3.4)

Ship & Evaluate — runbook (start/stop, secrets, kill-switch), micro-evaluation, observability (logging, tracing, audit trails), multi-agent patterns (4.1–5)

MC 19

AI Security & Ethics (Prompt Injection & More)

Protect your AI systems and build responsibly.

Foundation — why security & ethics matter, risk taxonomy for LLM apps, ethical principles & governance (1.1–1.3)

Attack Patterns — prompt injection & indirect injection, jailbreaks & safety bypass, data leakage/exfiltration, tool & function misuse (2.1–2.4)

Defense-in-Depth Guardrails — input hardening, tool-layer controls, output & policy enforcement, retrieval & RAG hygiene (3.1–3.4)

Runtime Hardening & Red Teaming — sandboxing, red-teaming & evaluation, metrics (attack success ↓, containment ↑, FP/FN, cost/latency) (4.1–4.3)

Governance & Compliance — data handling (minimization, retention, consent), bias review & appeals, policies, approvals & audit trail (5.1–5.3)

MC 20

GenAI Evaluation (RAG, Reasoning & Safety)

What separates AI demos from reliable AI products — full evaluation discipline.

🎓 Capstone Project

GenAI Evaluation Framework — analyze (task goals, failure hypotheses, datasets), measure, open coding & axial coding (1.1–1.4)

Evaluation Types — automatic (LLM-as-judge with safeguards), human-in-the-loop, task-specific (QA, summarization, code-gen, agents) (2.1–2.3)

Test Assets & Datasets — golden sets, adversarial sets, sampling & coverage, data ethics (3.1–3.4)

Metrics & Scoring — correctness (EM/F1/semantic), faithfulness (citation precision/recall), safety (leakage/jailbreak), ops (latency, cost, stability) (4.1–4.4)

RAG & Tool-Specific Evaluation — retrieval (Recall@k, MRR/NDCG), grounding & hallucination flags, tool schema conformance (5.1–5.3)

Automation & CI — eval harness (fixtures, seeds, traces), regression suites (canary prompts, dashboards), calibrating LLM-as-judge (6.1–6.3)

Also inside every week: assignments with feedback, quizzes with detailed answer explanations, expert workshops (FastAPI, Multimodal RAG, Chat-with-your-Database), soft skills, GenAI Ops foundations, deployment, and monitoring & troubleshooting. Pre-work + 2 years content access.

Why DDS

Not all AI programs are equal

Here’s how the AI Residency compares to a typical online course or bootcamp.

One-on-one mentorship

✅ Included

✗ Not included

Real, reviewed projects

✅ 3+ capstone builds

✗ Watch-only exercises

Live weekend sessions

✅ Every weekend

✗ Pre-recorded only

Curriculum depth

✅ 20 MCs · Agents to Evals

✗ Surface-level topics

Global community

✅ 180K+ · 50+ countries

✗ Forum only

Tools stack coverage

✅ 25+ real-world tools

✗ 1–3 tools max

CPD accreditation

✅ CPD Member Org

✗ Not accredited

Content access

✅ 2 years + recordings

✗ 6–12 months

110+

★★★★★

Verified learner reviews · Check Trustpilot →

★★★★★

The Residency exceeded all my expectations — LLMs, RAG pipelines, GenAI orchestration, mentorship that sharpened my thinking and presentation skills. I built projects mirroring real business challenges.

— Faizal · AI Residency Graduate

★★★★★

Learning during the AI cohort helped me win first place in an AI Hackathon. AI is not as scary as we think — I built apps including UI and deployment with a few lines of code.

— Adeeb · Resident

★★★★★

Rich networking, learning from peers and industry experts, and personalized advice from Mohammad Arshad. His guidance transcends traditional learning and drives real career growth.

— Mahesh · Community Member

★★★★★

World class teaching with unmatched passion. Mr Arshad is a passionate individual, a transformative teacher and has the most amazing heart-centred approach to community building I’ve come across.

— Oghale · Leader & Mentor

★★★★★

The course and content was brilliant, definitely a 5-star rating for me. I will recommend to everyone who is serious about doing data science jobs.

— Mariyam · Learner

★★★★★

The mentorship is great, very crisp to the point. And they don’t oversell like a lot of other companies do. Amazing community cohesion — a superb program overall.

— Yashvi · Resident

★★★★★

— Faizal · AI Residency Graduate

★★★★★

Learning during the AI cohort helped me win first place in an AI Hackathon. AI is not as scary as we think — I built apps including UI and deployment with a few lines of code.

— Adeeb · Resident

★★★★★

Rich networking, learning from peers and industry experts, and personalized advice from Mohammad Arshad. His guidance transcends traditional learning and drives real career growth.

— Mahesh · Community Member

★★★★★

World class teaching with unmatched passion. Mr Arshad is a passionate individual, a transformative teacher and has the most amazing heart-centred approach to community building I’ve come across.

— Oghale · Leader & Mentor

★★★★★

The course and content was brilliant, definitely a 5-star rating for me. I will recommend to everyone who is serious about doing data science jobs.

— Mariyam · Learner

★★★★★

The mentorship is great, very crisp to the point. And they don’t oversell like a lot of other companies do. Amazing community cohesion — a superb program overall.

— Yashvi · Resident

★★★★★

I never coded in my life, trying to learn SQL for a long time, but I mastered SQL within a month and got a job of 7 Lakhs per annum. Thanks for the wonderful course and helping freshers.

— Pawan · Career Switcher

★★★★★

30 years of experience in the computing/business world and I see the real value in this program. They’ve crafted a great curriculum that will increase your chances of getting a job.

— Patrick · Senior Professional

★★★★★

Mohammad Arshad gave me recommendations based on actual data analysis of my LinkedIn — not generic advice. That’s when I understood why people call him a data scientist.

— Hemant · Transformation Coach

★★★★★

The DDS Residency is community-driven and focused on practical outcomes — covering LLMs, RAG pipelines, data engineering, while helping participants build impactful projects under expert guidance.

— Iyad · Community Member

★★★★★

Amazing course! Puts python really simply, with great material in GitHub. Mr Arshad took my doubts seriously and uploaded a guide video for the final project. Amazing!

— Adam · Student

★★★★★

The LinkedIn challenge led by Mr. Arshad was a game-changer — covering everything from optimizing my profile to building meaningful connections. I felt empowered by the end.

— Muhammad · Professional

★★★★★

I never coded in my life, trying to learn SQL for a long time, but I mastered SQL within a month and got a job of 7 Lakhs per annum. Thanks for the wonderful course and helping freshers.

— Pawan · Career Switcher

★★★★★

30 years of experience in the computing/business world and I see the real value in this program. They’ve crafted a great curriculum that will increase your chances of getting a job.

— Patrick · Senior Professional

★★★★★

Mohammad Arshad gave me recommendations based on actual data analysis of my LinkedIn — not generic advice. That’s when I understood why people call him a data scientist.

— Hemant · Transformation Coach

★★★★★

The DDS Residency is community-driven and focused on practical outcomes — covering LLMs, RAG pipelines, data engineering, while helping participants build impactful projects under expert guidance.

— Iyad · Community Member

★★★★★

Amazing course! Puts python really simply, with great material in GitHub. Mr Arshad took my doubts seriously and uploaded a guide video for the final project. Amazing!

— Adam · Student

★★★★★

The LinkedIn challenge led by Mr. Arshad was a game-changer — covering everything from optimizing my profile to building meaningful connections. I felt empowered by the end.

— Muhammad · Professional

Free AI Community

Already in the community?
Residents get even more.

All AI Residency graduates get elevated access inside the world’s most active AI learning community.

📅Three live events every week (Zoom, LinkedIn Audio, Physical Meetups)

🎥50+ outcome-based workshop recordings & 45+ community meetup archives

📚Basic AI & DS courses — Excel, SQL, Python, Machine Learning, PowerBI

🤖GenAI resources — Prompt Engineering, LangChain, LLMs, Gemini MultiModal

📄Ready-to-use resume templates, sample reviewed resumes, LinkedIn optimization

💬24/7 WhatsApp & Discord — latest AI discussions, guidance from experts

Join Free AI Community →

What the community provides

🤖 Generative AI

ChatGPT for Business, Prompt Engineering, LangChain, LLMs, Gemini MultiModal

📊 Data Science Courses

Excel, SQL, Python (Basic & Advanced), PowerBI, Machine Learning, Knowledge Shorts

🎬 Recordings

50+ workshops · 45+ meetups · 10+ Python project videos · AI & DS career webinars

📁 Resources

GenAI resources, datasets, reviewed resume, LinkedIn optimization, Python & SQL docs

🌐 Join at nas.io/aiguild

Exclusive Bonuses

Unlock $5,100+ in additional value

Every Cohort 11 resident gets more than masterclasses — plus access to $11,538 worth of DDS Academy courses and products.

Bonus 01

AI Guild Content Access

$4,000

An exclusive library of AI tutorials, courses, workshop recordings, and industry insights to accelerate your learning.

Bonus 02

Extra One-on-One Mentorship

$600

3 additional hours of personal mentoring to refine your project, overcome blockers, and maximize your outcome.

Bonus 03

Cloud Credits

$500

Deploy and scale your AI applications — explore advanced cloud-based tools without infrastructure costs.

*Bonuses, scholarships, cloud credits, and partner opportunities are subject to eligibility, partner approval, project quality, submission completion, and final review. Values are indicative of comparable market pricing and do not represent cash.

Investment

One fee. Everything included.

20 masterclasses, 1-on-1 mentorship, capstone review, bonuses, and 2 years of content access.

AI Residency · Cohort 11

$3,000

Full 20-week program — all masterclasses, projects, mentorship, and bonuses included.

Up to 50% off · Code SCHOLARSHIP Monthly payment plans available

Reserve Your Seat in Cohort 11 →

30-day satisfaction guarantee · Scholarships subject to eligibility

AI Residency Program certificate of completion by Decoding Data Science — 135 hours of effort

Recognition

A certificate that means something

Complete the 20 masterclasses and your capstone to earn the AI Residency Certificate of Completion from Decoding Data Science — recognizing 135+ hours of structured effort. Showcase it on LinkedIn alongside your real, working portfolio.

Decoding Data Science is a CPD Member organization. Continuing Professional Development recognition strengthens the value of your certificate with employers worldwide.

Your Instructor

Mohammad Arshad

A globally recognized AI & data strategy expert with 21 years of industry experience. Mohammad has helped 5 of the world’s largest companies, 10 SMEs, and 3 startups build effective Data and AI strategies. Recognized by Accenture, HP, Dell, LinkedIn, and MAF. Founded Decoding Data Science in 2020 and DDS Academy in 2022. Teaching since 2008. Mentored 3,000+ individuals toward their career goals.

21 Years in Data & AI Founder — Decoding Data Science 60K+ LinkedIn Followers 100+ Keynotes Globally

Mentor Network

Get mentored by the future, today

Michael Stattelman

CTO · Falcons.ai

Priya M Nair

CEO · ZWAG AI Solutions

Dr Anish Roychowdhury

Data Science Consultant & Educator

Amr Mohamed Hassanin

Founder · CTO for Startups

María Carbajal

Tech Lead · Loud Intelligence

Safik Hossain

Data Science Manager

Tim Daines

Fractional Product Officer

Hemant Jain

Life & Career Transformation Coach

How to Join

Four steps to Cohort 11

Show Interest

Fill the short form — only 4 details — and tell us your goal and track.

Talk to the Team

Confused if the program is for you? Get a free consult and scholarship guidance.

Reserve Your Seat

Enroll with code SCHOLARSHIP while the window is open for up to 50% off.

Start 11 July

Join the exclusive resident community and begin MC 00 pre-work immediately.

FAQ

Frequently asked questions

This is a mentor-led residency, not a video course. You build real projects every phase — apps, RAG systems, agents — with 1-on-1 mentorship, live weekend sessions, and a capstone. You leave with proof of work, not just a certificate.

Yes. MC 00 covers basic Python and Gen AI fundamentals from scratch. The curriculum is the most beginner-friendly in Generative AI. Many residents started with zero coding background.

Live masterclasses run on weekends — practical for working professionals — supported by self-paced recordings, materials, and pre-work. You get 2 years of access to all content and recordings.

Two early projects (API-powered Python app + GenAI configuration build), a mid-capstone Weather/Stock Bot, RAG and fine-tuning assignments, agent builds with CrewAI and LangGraph, and a final capstone you present and showcase.

Yes — complete the masterclasses and capstone to earn the AI Residency Certificate of Completion from Decoding Data Science, a CPD Member organization recognizing 135+ hours of effort.

The program fee is $3,000. Use code SCHOLARSHIP at checkout for up to 50% off while the scholarship window is open. Monthly payment plans are available. Student and company scholarships are subject to eligibility and availability.

Students, working professionals, career switchers, founders, and agency owners — residents from 50+ countries have completed the program.

Yes — a 100% satisfaction guarantee: full refund within the first 30 days of enrollment, no questions asked, per the terms on the official program page.

Be a pioneer

Build the future with Cohort 11

Generative AI is reshaping every industry. Cohort 11 starts 11 July 2026. Your seat, your scholarship, your move.

Enroll Now · Code SCHOLARSHIP → Ask a Question First

*Scholarships, bonuses, and partner opportunities are subject to eligibility, availability, project quality, and final review. 30-day satisfaction guarantee applies per official terms.

🎓 Enroll · Code SCHOLARSHIP — 60:00

Go from zero to deployed GenAI builder in 20 weeks.

Not sure if the Residency is for you? Start here — free.

Which outcome are you building toward?

For Job Seekers

For Founders

For Agency Owners

20 Masterclasses · 20 Weeks

Phase 1 — Foundations & First Builds

Essential Prerequisite: Basic Python & What is Gen AI?

Application Architecture: UI Building, Git & GitHub

GROQ & Building Apps with APIs in Python

Intro to LLMs: How They Work (Playground)

Generative AI Configuration & Different LLMs

LLM Wrappers, Function Calls & Data Integration

Phase 2 — RAG & Knowledge Systems

Hugging Face: Building an Open-Source Project

LlamaIndex: Retrieval-Augmented Generation (RAG)

Vector Embeddings & Vector Databases

Advanced RAG

Phase 3 — Fine-Tuning & Reasoning

Tokenization & Preprocessing Data for Fine-Tuning

Data Preparation & Fine-Tuning (LoRA & QLoRA)

Chain of Thought & ReACT Prompting

LangChain

LangChain Advanced + Deployment

Phase 4 — Agents, Security & Evaluation

Multi-AI Agent Systems with CrewAI

LangGraph

Model Context Protocol & Agent-to-Agent

AI Security & Ethics (Prompt Injection & More)

GenAI Evaluation (RAG, Reasoning & Safety)

Not all AI programs are equal

Already in the community?Residents get even more.

Unlock $5,100+ in additional value

AI Guild Content Access

Extra One-on-One Mentorship

Cloud Credits

One fee. Everything included.

A certificate that means something

Mohammad Arshad

Get mentored by the future, today

Michael Stattelman

Priya M Nair

Dr Anish Roychowdhury

Amr Mohamed Hassanin

María Carbajal

Safik Hossain

Tim Daines

Hemant Jain

Four steps to Cohort 11

Show Interest

Talk to the Team

Reserve Your Seat

Start 11 July

Frequently asked questions

Build the future with Cohort 11

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