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Each batch has its own dates, timing and fee. Pick one to see its details and book.
Live weekend classes
AI & Data Science Program
Live weekend classes and 12 industry projects, from your first line of Python to job-ready AI engineering.
- 12 months
- Sundays
- 3 hours a week
- 12 industry projects
- 14 industry tracks to choose from
- Beginner → Advanced → Expert
Learn AI and data science on weekends, alongside college or work. Every month you learn one area in live classes and finish an end-to-end project on real industry data, so by the end you have 12 projects to show employers. Classes are live and recorded, taught by industry practitioners, in small batches.
Fees
Monthly
₹2,999 / month
Pay each month (UPI AutoPay or card). Cancel any time; access runs to the end of the paid month.
One-time
₹32,390 for 12 months
The whole program, plus recordings of earlier batches.
How joining works
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1
Book a free seat
No payment now. Your seat is held in the next batch.
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2
Pay when the batch is confirmed
We email you; pay within 48 hours to keep your seat.
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3
Join live weekend classes
Classes are recorded, so you can catch up on anything you miss.
Choose your industry track
Everyone learns the same AI skills each month. Your 12 projects come from the industry you choose, so you finish with a portfolio for the jobs you want.
Pharma & Life Sciences: AI speeds up drug discovery, predicts molecule properties, keeps manufacturing quality in check and watches drug safety.
Free courses and datasets for Pharma & Life Sciences →Month by month
Every month ends with an end-to-end project you can show employers, from Pharma & Life Sciences.
Month 1 Python for data Beginner
Python basics, data types, loops and functions; working with files, CSV and JSON; virtual environments, Jupyter and Git.
Project · Pharma & Life Sciences
Lab results processor
Read instrument exports (such as HPLC results), calculate assay values and flag any batch that is out of specification.
Month 2 Data analysis with pandas and SQL Beginner
pandas DataFrames, cleaning and joining data, group-by and pivot tables; SQL queries, joins and window functions.
Project · Pharma & Life Sciences
Medicine sales analysis
Analyse medicine sales by molecule, therapy area and region with SQL and pandas, and find the fastest-growing segments.
Month 3 Statistics and data visualisation Beginner
Descriptive statistics, probability, sampling, hypothesis tests and A/B testing; charts with Matplotlib, Seaborn and Plotly.
Project · Pharma & Life Sciences
Clinical trial statistics
Compare the arms of a published clinical trial with confidence intervals and hypothesis tests, and chart the outcome.
Month 4 Machine learning foundations Beginner
Supervised learning, regression and classification, train/test splits and cross-validation, scikit-learn pipelines.
Project · Pharma & Life Sciences
Molecule solubility predictor
Predict how well molecules dissolve from their chemical descriptors (RDKit and an open solubility dataset).
Month 5 Feature engineering and model evaluation Advanced
Feature engineering, imbalanced data, ensembles (random forests, XGBoost, LightGBM), metrics, explainability with SHAP.
Project · Pharma & Life Sciences
Batch quality failure model
Predict which manufacturing batches will fail quality checks from process settings, and use SHAP to find likely causes.
Month 6 Deep learning with PyTorch Advanced
Neural networks, training loops, CNNs for images, transfer learning, GPUs on Google Colab.
Project · Pharma & Life Sciences
Tablet defect inspection
Train an image model that spots chipped, cracked or discoloured tablets and capsules on an inspection line.
Month 7 Natural language processing Advanced
Text cleaning, embeddings, transformers and Hugging Face, fine-tuning for classification, multilingual models.
Project · Pharma & Life Sciences
Adverse event extractor
Pull drug and side-effect pairs out of safety reports for pharmacovigilance, and rank the most reported ones.
Month 8 Time series and forecasting Advanced
Trends and seasonality, ARIMA and Prophet, gradient-boosted forecasts, evaluating forecasts over time.
Project · Pharma & Life Sciences
Medicine demand forecast
Forecast monthly demand for common medicines, including dengue and flu seasons, to plan production and stock.
Month 9 Large language models and RAG Expert
How LLMs work, prompting, embeddings and vector databases, retrieval-augmented generation, evaluating answers.
Project · Pharma & Life Sciences
Regulatory guidelines assistant
Build a RAG assistant over CDSCO and ICH guidelines that answers regulatory questions with exact citations.
Month 10 AI agents and tool use Expert
Function calling, multi-step agents, planning and memory, guardrails and evaluation of agent behaviour.
Project · Pharma & Life Sciences
Literature research agent
Create an agent that searches PubMed abstracts on a drug target, summarises the evidence and cites every paper.
Month 11 MLOps and deployment Expert
APIs with FastAPI, Docker, CI/CD, model registries, monitoring and data drift, cloud deployment basics.
Project · Pharma & Life Sciences
Validated model in production
Deploy a quality model with version control, an audit trail and change records, as regulated (GxP) work requires.
Month 12 Capstone and career preparation Expert
Scoping a real problem, project planning, portfolio, résumé and GitHub reviews, mock technical interviews.
Project · Pharma & Life Sciences
Pharma capstone
Solve a real drug discovery, quality or safety problem end to end with a mentor and present it to a panel.
What you can watch
| Monthly | Your batch's live classes and recordings, from the batch start to the current month, while your payments are up to date. |
|---|---|
| One-time | Your whole batch, plus recordings of earlier batches. |
| Joining a running batch | That batch's earlier recordings, so you can catch up. |
| After you finish | 12 more months of free access. |