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AI-X

Artificial Intelligence, Machine Learning & Generative AI

Go beyond using AI tools to actually building them.

Co-developed with iHub Anubhuti–IIITD

Next-Gen Internet & Computing

Duration

~3 months

Format

Fully online

Schedule

1.5-hour live classes on alternate days

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Why this programme

Go beyond using AI tools to actually building them.

A practical, project-backed route from machine-learning fundamentals to working Generative AI applications. Build and ship models and LLM-based tools, with responsible AI in view—not heavy theory for its own sake.

Your starting point

Designed for students from any branch with basic Python and school-level maths. Some familiarity with AI and ML is useful, but you do not need to have built or deployed a model; prior LLM API, RAG or agent experience is not expected.

These notes describe the programme’s intended starting point, not a formal eligibility table. Working professionals are welcome to enquire; no additional entry criteria are implied here.

What you can take forward

  • Build, evaluate and tune machine-learning and deep-learning models on real datasets.
  • Create LLM applications with prompting, RAG and simple agents, then deploy them as web apps.
  • Explain bias, privacy and responsible-AI concerns in plain language.
  • Present a portfolio of module assignments and an industry-oriented capstone.

Curriculum map

Ten modules. One working outcome.

Core topics and module build tasks are the programme pathway. Optional extensions are marked separately and are not promised core instruction.

Core learningHands-on buildOptional extension
01Python and Maths for AI

Core topics

Python functions and lists, Pandas, simple charts, and intuitive averages and proportions using Colab.

Build task

Load, clean and visualise a small dataset.

Optional extension · not a core promise

Extended linear algebra.

02Machine Learning Foundations

Core topics

Regression and classification, train-test split, a scikit-learn baseline and predictions.

Build task

Create a working predictor for new records.

03Model Evaluation and Improvement

Core topics

Confusion matrix, accuracy, precision and recall, data leakage and error inspection.

Build task

Evaluate predictions, identify errors and document improvements.

Optional extension · not a core promise

ROC-AUC and tuning.

04Advanced ML Techniques

Core topics

Compare decision trees or random forests with a simple baseline; interpret and compare results.

Build task

Compare two models and justify a choice.

Optional extension · not a core promise

SVM, boosting, PCA and clustering.

05Neural Networks and Deep Learning

Core topics

Intuitive neural-network concepts; use a pretrained model or a small guided network.

Build task

Run, modify and inspect a neural-network notebook.

Optional extension · not a core promise

Backpropagation mathematics and advanced training.

06NLP and Transformers

Core topics

Clean text, embeddings and semantic similarity; a conceptual explanation of transformers.

Build task

Build a small semantic text-search app.

Optional extension · not a core promise

BERT fine-tuning.

07Generative AI and LLMs

Core topics

LLM prompting, API calls, structured output, tokens and cost, hallucinations and simple test cases.

Build task

Create a structured extraction or summarisation tool.

Optional extension · not a core promise

LoRA.

08RAG and AI Agents

Core topics

One RAG workflow covering chunking, retrieval, source references and simple evaluation.

Build task

Build document Q&A with cited sources.

Optional extension · not a core promise

MCP, orchestration frameworks and multi-agent systems.

09MLOps, Deployment and Responsible AI

Core topics

Deploy a simple Streamlit app, test outputs, handle errors, and discuss privacy and bias.

Build task

Share a tested app with a short limitations note.

Optional extension · not a core promise

Docker and cloud MLOps.

10Capstone Project Capstone begins in module 8

Core topics

An individually adapted, useful ML or LLM application.

Capstone deliverables

Deliver runnable code, sample data, evaluation, error analysis and documentation; present the project and complete a technical viva.

Tools named as examples or recommendations in the outline are illustrative; the teaching approach may use one supported tool or stack where indicated.

Portfolio & capstone

Evidence you can show

Runnable code, sample data, evaluation results, error analysis and documentation for an individually adapted ML or LLM application; finish with a project presentation and individual technical viva.

Learning rhythm

One hands-on assignment per module, quizzes and a mid-programme assessment. Guided work includes structured and recorded content, live sessions, exercises, coding practice and case studies.

Illustrative next steps

Where this can lead

Entry-level pathways to explore—not placement promises.

  • ML engineer trainee
  • AI / GenAI developer
  • AI analyst
  • Further study in AI and machine learning

Certification

Certification with iHub Anubhuti–IIITD

Co-developed with iHub Anubhuti–IIITD

Delivery & assessment

Duration

~3 months

Format

Fully online

Live classes

1.5 hrs, alternate days

Modules

10, capstone from module 8

Each programme runs about 3 months, fully online, with 1.5-hour live classes on alternate days and about 30–35 hours of live instruction. Structured and recorded content sits alongside hands-on exercises, coding work, case studies, quizzes and guided projects. There is one hands-on assignment per module plus quizzes and a mid-programme assessment. The capstone begins in module 8, includes mentor checkpoints, and concludes with a final demo, assessment and individual viva. Certification considers assignments, the assessment, capstone and a minimum attendance requirement.

One assignment per moduleQuizzes + mid-programme assessmentCapstone + individual vivaMinimum attendance requirement

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