About this course
A complete machine-learning bootcamp: Python for data, statistics, EDA, every major algorithm with intuition and maths, NLP from bag-of-words to transformers, and deploying models with Git, Docker and a cloud host.
What you will be able to do
- Run a full ML workflow from raw data to deployed model
- Understand the maths behind core algorithms
- Engineer features and tune models properly
- Build NLP pipelines from classic to transformer-based
- Package and deploy models with Docker
Curriculum
01Python for data
- NumPy
- pandas
- Visualisation
02Statistics & EDA
- Distributions
- Hypothesis tests
- Exploratory analysis
03Supervised learning
- Linear & logistic models
- Trees & ensembles
- SVMs
04Unsupervised learning
- Clustering
- PCA
- Anomaly detection
05Model quality
- Validation
- Tuning
- Explainability
06NLP
- Text preprocessing
- Word embeddings
- Transformers
07Deployment
- APIs
- Docker
- CI/CD basics
Tools you will use
Prerequisites
- None — we start from Python basics
Projects you will build
5 milestones
Telecom customer churn prediction
Predict which customers are likely to leave, explain why, and turn it into a retention list the business can act on.
pandas · scikit-learn · XGBoost Coming soon
5 milestones
Two-stage loan approval and amount estimation
Stage one decides approve or decline; stage two estimates how much to lend — with fairness checks across applicant groups.
scikit-learn · LightGBM · Fairlearn Coming soon
5 milestones
Thunderstorm forecasting with MLflow tracking
Forecast thunderstorm risk from weather-station data and track every experiment, parameter and model version with MLflow.
pandas · scikit-learn · MLflow Coming soon