Machine Learning & Data Science
Learn machine learning the way it's used at work: start from messy real data, build and evaluate models properly, then ship them as an API someone can actually use. Every module ends with a working notebook or service.
Who this is for
- Final-year students and graduates from any branch who can write basic code
- Analysts and developers moving into ML or data roles
- Engineers who want to use their domain data (sensors, energy, manufacturing)
What you'll be able to do
- Clean, explore and visualise real datasets
- Train, tune and fairly evaluate ML models
- Build neural networks for images, text and time series
- Deploy a model as an API with monitoring basics
- Explain your model choices in an interview
Syllabus
8 modules. Every module has lab sessions on real boards or tools.
M01Python and data foundations
- Python, NumPy and pandas
- Jupyter and Git workflow
- Visualisation with Matplotlib and Seaborn
M02Statistics for ML
- Distributions, sampling and hypothesis testing
- Correlation, bias and data leakage
M03Supervised learning
- Linear and logistic regression
- Decision trees, random forests, gradient boosting
- Cross-validation, metrics and tuning
M04Unsupervised learning
- Clustering and dimensionality reduction
- Anomaly detection on sensor data
M05Deep learning with PyTorch
- Neural network fundamentals
- CNNs for images
- Sequence models for time series
M06NLP and generative AI
- Text classification and embeddings
- Transformers and LLM fine-tuning basics
- Retrieval-augmented generation (RAG)
M07MLOps and deployment
- Serving models with FastAPI
- Docker basics, model versioning and monitoring
M08Capstone project
- End-to-end project on a real dataset
- Presentation and code review
Projects you can build
Energy consumption forecast
Predict building energy use from meter and weather data.
Defect detection
Spot faulty parts from images with a CNN.
Support ticket classifier
Route customer messages automatically, served as an API.
Tools and technologies
- Python
- pandas
- scikit-learn
- PyTorch
- FastAPI
- Git
Every program includes
- Mock technical and HR interviews with industry experts
- Resume, LinkedIn and GitHub review
- Course completion certificate after the final assessment
- Referrals to our hiring network
Questions
How much maths do I need?
School-level maths is enough to start. We teach the statistics and linear algebra you need, with code rather than proofs.
Is this different from the Applied AI course?
Yes. Machine Learning & Data Science goes deeper into building and evaluating models yourself. Applied AI is shorter and focuses on adding ready-made AI and LLMs to products.
Do I need a GPU laptop?
No. We use free cloud notebooks with GPUs for deep learning.