Intermediate10 weeks3 modules
Machine Learning Engineer
Models you can explain, code you can ship
Learn machine learning the practical way: implement the core algorithms, understand the math just deeply enough to debug them, and then use scikit-learn and PyTorch the way working ML engineers do. Every module ends with a project on a real dataset.
Curriculum
1Data & Classical ML
- NumPy, pandas, and exploratory data analysis
- Linear and logistic regression from scratch
- Trees, ensembles, and gradient boosting
- Cross-validation and honest evaluation
2Deep Learning
- Neural networks and backpropagation
- PyTorch fundamentals and training loops
- CNNs and transfer learning
- Regularization, optimization, and debugging training
3ML in Production
- Feature pipelines and data leakage
- Experiment tracking and reproducibility
- Serving models behind an API
- Capstone: end-to-end prediction service