Technical / ITData & AI▶ Live code
Data science & AI/ML
Python, statistics, machine learning with scikit-learn, and an introduction to deep learning and LLMs.
Who it’s for: Learners comfortable with Python and maths who want ML and AI roles.
Roadmap
What you’ll learn
Follow the modules in order. Finish a small project at the end of each one.
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01
Python for data
3 weeks- NumPy and pandas
- Data cleaning
- Visualisation
- Jupyter notebooks
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02
Statistics & maths
3 weeks- Probability and distributions
- Hypothesis testing
- Linear algebra basics
- Gradient intuition
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03
Machine learning
6 weeks- Regression and classification
- Train/validation/test and cross-validation
- Metrics: precision, recall, F1
- Feature engineering
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04
Deep learning & AI
4 weeks- Neural network basics
- NLP and embeddings
- Using LLM APIs responsibly
- Model deployment basics
Tools
Tools & skills to learn
- Python
- pandas
- scikit-learn
- Jupyter
- TensorFlow or PyTorch
- Git
Certifications
Useful certifications
- NPTEL courses in ML/AIProctored exam certificate
- Cloud ML fundamentals (Azure AI-900)Paid; entry level
Certificates support your profile — interviewers still ask what you built. Check current fees and rules on the official site.
Practice
Projects to build
Put these on your resume — they give you real stories for interviews.
- 1House-price prediction with clear evaluation
- 2Spam classifier with precision/recall analysis
- 3Resume–job matching using text similarity
Live code
Practise right here
Run examples and try the 🏆 challenges — your answers are checked automatically.
Preview
Console
Sample tables: employees(id, name, dept_id, salary, joined) · departments(id, name) · users(id, email)
Result
Press Run to see the result.
Output
Code runs only in your browser. Press Ctrl/⌘ + Enter to run. Pick a 🏆 Challenge from the Example list to get your answer checked automatically.
Next steps
Interview & jobs
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