Core AI: Build AI
Seven modules over 14 weeks: maths, data engineering, machine learning, deep learning, NLP and LLMs, computer vision and MLOps, with runnable code and Indian datasets.
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Who it's for
- CS graduates and developers pivoting into AI/ML
- Engineers who want to understand models, not just call APIs
- Career-switchers willing to put in about 14 weeks of focused effort
What you'll be able to do
- Explain and apply the maths behind ML with code
- Build data pipelines and features in Python and pandas
- Train, evaluate and select classical ML models
- Build neural networks by hand, then in PyTorch
- Work with tokenisation, embeddings, Transformers and LLM APIs; fine-tune models
- Build vision systems: detection, segmentation, OCR and multimodal
- Track experiments, serve models, monitor and ship with CI/CD
7 modules, 28 sections
MODULE 1Mathematical Foundations
Weeks 1-2. The maths behind everything, built through visual, code-first exercises.
- Linear algebra
- Calculus and optimisation
- Probability and statistics
- Information theory
Module project: PCA image compression
MODULE 2Programming and Data Engineering
Weeks 3-4. Python proficiency and the data pipeline that feeds every model.
- Python and pandas
- Data acquisition and cleaning
- Feature engineering
- Storage and versioning
Module project: Indian real-estate dataset pipeline
MODULE 3Machine Learning
Weeks 5-6. Classical ML and the full train-evaluate-iterate cycle.
- Regression
- Classification
- Unsupervised learning
- Model selection
Module project: NBFC credit-risk scorer
MODULE 4Deep Learning
Weeks 7-8. From a single perceptron to Transformers, by hand first, then in PyTorch.
- Neural network fundamentals
- Training deep networks with PyTorch
- CNNs and transfer learning
- Sequence models and Transformers
Module project: Hinglish sentiment: LSTM vs Transformer
MODULE 5NLP and Large Language Models
Weeks 9-10. From tokenisation to building on GPT-class models.
- Text representations
- LLM architecture and APIs
- Fine-tuning and adaptation
- NLP applications
Module project: Multilingual customer-support chatbot
MODULE 6Computer Vision
Weeks 11-12. Image and video understanding up to vision-language models.
- Image fundamentals
- Detection and segmentation
- Vision transformers and generative models
- Video, multimodal and OCR
Module project: Smart retail shelf analyser
MODULE 7MLOps and Deployment
Weeks 13-14. The bridge from notebook to production that most courses skip.
- Experiment tracking and reproducibility
- Model serving
- Infrastructure and scaling
- Monitoring, CI/CD and capstone
Module project: End-to-end MLOps capstone on the credit-risk model
Learn
Each section opens with clear learning objectives tied to where the skill shows up in a 2026 job in India, then 12+ expandable teaching cards.
Try
"Try it yourself" prompts you paste into Claude or ChatGPT and runnable code with real output.
Prove
A 10-question quiz per section. Pass at 80% to unlock the next.
Ready to start?
Not sure? Take the free 100-question assessment to see which track fits.