Code School of Analytics
Artificial Intelligence

Become an Artificial Intelligence Developer

Plenty of people can paste a prompt into a chatbot. Far fewer can build the thing behind it: clean the data, train and test a model, connect a language model to a company's own documents, and put the result on a server…

Physical
Mentor Faisal Adnan12 weeksOnsite Monday to Friday, 3:00-6:00 PM or 6:00-9:00 PMOnsite (Islamabad campus)180 credit hoursEnglish / UrduLast updated 14 Oct 2026

Course description

Plenty of people can paste a prompt into a chatbot. Far fewer can build the thing behind it: clean the data, train and test a model, connect a language model to a company's own documents, and put the result on a server where it doesn't fall over on the second user. That gap is where the AI developer jobs are, and it's what these twelve weeks are built around.

This is an onsite course on our Islamabad campus. You come in five days a week, Monday to Friday, for three hours a day, in either the 3:00-6:00 PM or the 6:00-9:00 PM batch. That's fifteen hours of class a week and 180 credit hours in total, with a mentor in the room for every lab. We start with Python and data handling, move through classical machine learning and deep learning, then spend the second half on language models, retrieval, agents and deployment. The last week is a capstone you build, demo and put on GitHub.

You should be able to write a loop and a function in some language before you start. Python is taught from the level you need in the first two weeks, so you won't be lost, but the pace after that assumes you can keep up with code every day. Expect homework most evenings. Students who treat this like a part-time hobby fall behind by week four, and we'd rather tell you that now.

Twelve-week roadmap: Python and data, classical machine learning, deep learning and language models, then RAG, agents, deployment and a capstone

Class schedule

Days & time
Monday to Friday, 3:00-6:00 PM · 12 weeks
Duration
12 weeks
Mode
Physical
Next start
09 Nov 2026

Upcoming batches

PhysicalNext start

AIDEV-Weekday-Onsite-3to6

Batch fee

Rs 60,000

Starts

09 Nov 2026

Ends

01 Feb 2027

Class days & time

Monday to Friday, 3:00-6:00 PM · 12 weeks

Mentor

Faisal Adnan

20 seats left0 / 20 enrolled

Enroll by 07 Nov 2026

Enroll in this batch
Physical

AIDEV-Weekday-Onsite-6to9

Batch fee

Rs 60,000

Starts

09 Nov 2026

Ends

01 Feb 2027

Class days & time

Monday to Friday, 6:00-9:00 PM · 12 weeks

Mentor

Faisal Adnan

20 seats left0 / 20 enrolled

Enroll by 07 Nov 2026

Enroll in this batch

What You'll Learn

Write clean Python for data and AI work, with virtual environments, Git and small tested functions instead of one giant notebook
Clean, join and explore real tabular data with NumPy and pandas, and spot the silent mistakes that ruin a model before it is trained
Split data properly, avoid leakage and choose metrics that match the business question, not the ones that look best
Build, tune and compare regression, classification, tree and ensemble models with scikit-learn pipelines
Train a neural network in PyTorch, read a loss curve, and recognise overfitting before your validation score tells you
Use transformers and the Hugging Face libraries, and decide honestly between prompting, retrieval and fine-tuning
Build a retrieval-augmented generation system over English and Urdu documents, then measure whether it actually answers from the source
Wrap models in a FastAPI service, containerise it with Docker, add logging and monitoring, and handle user data carefully
Certificate on passing the final assessmentFive classes a week, Monday to Friday, three hours each, on the Islamabad campusTwo batches to choose from: 3:00-6:00 PM or 6:00-9:00 PMA weekly quiz plus a final assessment so you know where you standLabs use local examples: Urdu and English documents, PKR-based business data and Pakistani use casesA capstone project on GitHub, with a CV review and interview practice in the last week

Skills You'll Gain

Python
NANumPy and pandas
Statistics for ML
Sscikit-learn
FEFeature engineering
PPyTorch
HFHugging Face Transformers
LALLM APIs and prompting
RARAG and vector search
FAFastAPI and Docker

Test Yourself

Week 1 Quiz: Python for AI

Five questions on Python basics, environments and Git. Take it after Day 5 of Week 1.

5 questions

Week 2 Quiz: NumPy and pandas

Five questions on arrays, DataFrames, cleaning and joins.

5 questions

Week 3 Quiz: Statistics and the ML workflow

Five questions on splits, leakage, baselines and metrics.

5 questions

Week 4 Quiz: Supervised learning with scikit-learn

Five questions on models, pipelines and tuning.

5 questions

Week 5 Quiz: Unsupervised learning, features and evaluation

Five questions on clustering, PCA, cross-validation and imbalanced data.

5 questions

Week 6 Quiz: Deep learning foundations

Five questions on the training loop, overfitting and CNNs.

5 questions

Week 7 Quiz: NLP and transformers

Five questions on tokens, embeddings, attention and choosing an approach.

5 questions

Week 8 Quiz: Large language models in practice

Five questions on prompting, structured output, hallucination and safety.

5 questions

Week 9 Quiz: Retrieval-augmented generation

Five questions on chunking, retrieval, reranking and evaluating RAG.

5 questions

Week 10 Quiz: AI agents and tool use

Five questions on agent loops, function calling and guardrails.

5 questions

Week 11 Quiz: Shipping AI

Five questions on APIs, containers, monitoring and privacy.

5 questions

Week 12 Quiz: Capstone, portfolio and career

Five questions on presenting your work and talking about it honestly.

5 questions

Final Assessment: Become an Artificial Intelligence Developer

Thirty questions covering all twelve weeks. You need 70 percent to pass and earn the certificate. You have 75 minutes.

30 questions

Clean and explore a messy business dataset

Take a retail or telecom export with wrong types, duplicates and blanks, clean it with pandas, join it to a second table, and write down every assumption you made. Then check the row count after each join. Someone always finds a silent duplication here.

Hands-on Lab Task

Build a leak-free prediction pipeline

Predict customer churn or loan default with a scikit-learn Pipeline, split the data the right way, compare three models against a dumb baseline and explain which metric you chose and why. Deliberately introduce a leak first and see how good it makes the score look.

Hands-on Lab Task

Train a small neural network in PyTorch

Write the training loop yourself, plot training and validation loss, and fix an overfitting model with early stopping and dropout. No high-level trainer until you have seen what it hides.

Hands-on Lab Task

Classify and search text with Hugging Face

Fine-tune or reuse a small pretrained model on a text classification task, then compare it against a plain prompt on the same examples. Measure both on a held-out set, not on the examples you like.

Hands-on Lab Task

Build a document question-answering assistant

Index a set of English and Urdu circulars, retrieve with embeddings, add a reranking step, return answers with citations, and measure retrieval quality on a list of questions you wrote yourself. Include questions the documents cannot answer.

Hands-on Lab Task

Deploy an AI service with monitoring

Wrap your best model or assistant in a FastAPI app, containerise it with Docker, add request logging and a health check, then deploy it and break it on purpose with bad input to see what your logs tell you.

Hands-on Lab Task

Fee by mode

Onsite / physicalRs 60,000
Duration12 weeks
Class scheduleMonday to Friday, 3:00-6:00 PM · 12 weeks
ModeOnsite
Modules12
Quizzes13
Hands-on projects—
Skill levelIntermediate
CertificateYes

Next batches

09 Nov 2026Next start

AIDEV-Weekday-Onsite-3to6

Onsite20 seats leftMonday to Friday, 3:00-6:00 PM · 12 weeks

09 Nov 2026

AIDEV-Weekday-Onsite-6to9

Onsite20 seats leftMonday to Friday, 6:00-9:00 PM · 12 weeks

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8 weeks48 credit hrs8 modules2 batches
MentorFaisal AdnanfromRs 15,000