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AI & Machine Learning · 14-Week Program

Build intelligence.
Deploy it.

Go from Python fundamentals to deploying real ML and GenAI solutions on actual client problems, not toy datasets and graduate with models in production.

0Program duration
0Placement rate
0Models deployed live
0Real-problem learning
Program Curriculum

Python foundations to production AI in 14 weeks.

A structured journey through the full ML lifecycle data prep, model building, deep learning, GenAI and cloud deployment on real-world problems.

Weeks 1–2

Python for ML

Python essentials, NumPy, Pandas and data wrangling the foundation every ML engineer needs before touching models.

PythonPandas
Weeks 3–5

Machine Learning Algorithms

Regression, classification, clustering, decision trees, ensemble methods and model evaluation with Scikit-learn.

Scikit-learnModel Eval
Weeks 6–8

Deep Learning & Neural Networks

Build and train neural networks with TensorFlow and PyTorch CNNs, RNNs and transfer learning on real data.

TensorFlowPyTorch
Weeks 9–10

NLP & Generative AI

Text classification, sentiment analysis, LLM fine-tuning, RAG pipelines and building GenAI-powered applications.

LLMs & RAGNLP
Weeks 11–12

MLOps & Model Deployment

Serve models via REST APIs with FastAPI, containerise with Docker, deploy to AWS and monitor in production.

FastAPIAWS
Weeks 13–14

Capstone & Career Sprint

Build and deploy a production-ready AI solution for a real business problem, then present to an industry panel.

Live ProjectPlacement
Real
models, real impact
Why learn AI & ML here

You deploy models to real systems not notebooks

GenieBox builds AI-powered features into real client products. Students contribute to those features training models on actual data, fine-tuning LLMs for live use cases and deploying solutions that end users interact with. You graduate with shipped AI work, not just Jupyter notebooks.

  • Contribute AI features to live client products
  • Mentorship from practising ML engineers
  • Models deployed to production AWS infrastructure
  • Placement support with AI-first companies and startups
Student Services

Support that goes beyond the syllabus

Internship Program

Intern as an ML engineer on real AI features inside GenieBox client products.

Production Model Projects

Build models that ship to real users not sandbox experiments that disappear after the program.

Placement Assistance

Resume, ML interview prep and referrals to AI-first companies and analytics teams.

Career Guidance

1:1 mentorship to choose your specialisation ML engineer, data scientist or AI researcher.

Corporate Training

AI adoption and GenAI integration training for product and engineering teams.

Certification

GenieBox Academy certificate with a verified portfolio of deployed models and AI projects.

Placement Stories

From learners to ML engineers

"

I joined with a basic Python background and left having deployed an NLP model to a real product. The hiring manager at my current company said they'd never seen a fresher come in with a model actually running in production. That's what set me apart.

PT
Priya T.ML Engineer
"

The GenAI and RAG module was unlike anything I found in online courses. We built a real retrieval-augmented chatbot that's still running on a client's platform. Walking into interviews with that kind of project changes the entire conversation.

VC
Vinay C.AI Engineer Product Startup
FAQ

Before you enrol

Do I need a maths or CS degree to join?
No degree is required. A basic comfort with numbers and some familiarity with programming helps, but we start Python from fundamentals. The maths is introduced intuitively and practically - no heavy linear algebra upfront.
What frameworks and tools will I learn?
Python, NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, HuggingFace Transformers, LangChain, FastAPI, Docker and AWS SageMaker. The stack reflects what real ML teams use in production today.
What makes this different from online ML courses?
Most courses give you toy datasets and notebook exercises. Here you work on real client problems, get code-reviewed by practising engineers and deploy models to actual infrastructure. The outcome is a portfolio of shipped AI work, not certificates.
Does the program cover Generative AI?
Yes, Two full weeks are dedicated to LLMs, prompt engineering, RAG pipelines and building GenAI-powered applications. This is integrated into a real project, not a standalone theoretical module.
What kind of roles do graduates get placed in?
ML Engineer, Data Scientist, AI Engineer, NLP Engineer and MLOps Engineer. Our graduates have joined AI-first startups, analytics consultancies and the data teams of established product companies. Placement support is active for 6 months post-graduation.

Your AI career
starts here.

Book a free counselling session. We'll assess your background, walk through the program and find the right cohort for your goals.

Free course counselling
Flexible batches & payment plans
Placement & internship support

Course counselling

Tell us what you'd like to learn.